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LLM Engineer's Handbook
LLM Engineer's Handbook
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
By Paul Iusztin
October 2024 | 522 pages
Icon Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning
Icon Learn essential skills for deploying and monitoring LLMs, ensuring optimal performance in production
Icon Utilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applications
Understanding the LLM Twin Concept and Architecture Chevron down icon Chevron up icon
Understanding the LLM Twin Concept and Architecture Understanding the LLM Twin concept Planning the MVP of the LLM Twin product Building ML systems with feature/training/inference pipelines Designing the system architecture of the LLM Twin Summary References
Tooling and Installation Chevron down icon Chevron up icon
Tooling and Installation Python ecosystem and project installation MLOps and LLMOps tooling Databases for storing unstructured and vector data Preparing for AWS Summary References
Data Engineering Chevron down icon Chevron up icon
Data Engineering Designing the LLM Twin’s data collection pipeline Gathering raw data into the data warehouse Summary References
RAG Feature Pipeline Chevron down icon Chevron up icon
RAG Feature Pipeline Understanding RAG An overview of advanced RAG Exploring the LLM Twin’s RAG feature pipeline architecture Implementing the LLM Twin’s RAG feature pipeline Summary References
Supervised Fine-Tuning Chevron down icon Chevron up icon
Supervised Fine-Tuning Creating an instruction dataset Creating our own instruction dataset Exploring SFT and its techniques Fine-tuning in practice Summary References
Fine-Tuning with Preference Alignment Chevron down icon Chevron up icon
Fine-Tuning with Preference Alignment Understanding preference datasets Creating our own preference dataset Preference alignment Implementing DPO Summary References
Evaluating LLMs Chevron down icon Chevron up icon
Evaluating LLMs Model evaluation RAG evaluation Evaluating TwinLlama-3.1-8B Summary References
Inference Optimization Chevron down icon Chevron up icon
Inference Optimization Model optimization strategies Model parallelism Model quantization Summary References
RAG Inference Pipeline Chevron down icon Chevron up icon
RAG Inference Pipeline Understanding the LLM Twin’s RAG inference pipeline Exploring the LLM Twin’s advanced RAG techniques Implementing the LLM Twin’s RAG inference pipeline Summary References
Inference Pipeline Deployment Chevron down icon Chevron up icon
Inference Pipeline Deployment Criteria for choosing deployment types Understanding inference deployment types Monolithic versus microservices architecture in model serving Exploring the LLM Twin’s inference pipeline deployment strategy Deploying the LLM Twin service Autoscaling capabilities to handle spikes in usage Summary References
MLOps and LLMOps Chevron down icon Chevron up icon
MLOps and LLMOps The path to LLMOps: Understanding its roots in DevOps and MLOps Deploying the LLM Twin’s pipelines to the cloud Adding LLMOps to the LLM Twin Summary References
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
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C# 13 and .NET 9 – Modern Cross-Platform Development Fundamentals
C# 13 and .NET 9 – Modern Cross-Platform Development Fundamentals
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By Mark J. Price
November 2024 | 828 pages
Icon Explore the newest additions to C# 13, the .NET 9 class libraries, and Entity Framework Core 9
Icon Build professional websites and services with ASP.NET Core 9 and Blazor
Icon Enhance your skills with step-by-step code examples and best practices tips
Hello, C#! Welcome, .NET! Chevron down icon Chevron up icon
Hello, C#! Welcome, .NET! Introducing this book and its contents Setting up your development environment Understanding .NET Building console apps using Visual Studio Building console apps using VS Code Making good use of the GitHub repository for this book Looking for help Practicing and exploring Summary
Speaking C# Chevron down icon Chevron up icon
Speaking C# Introducing the C# language Discovering your C# compiler version Understanding C# grammar and vocabulary Working with variables Exploring more about console apps Practicing and exploring Summary
Controlling Flow, Converting Types, and Handling Exceptions Chevron down icon Chevron up icon
Controlling Flow, Converting Types, and Handling Exceptions Operating on variables Understanding selection statements Understanding iteration statements Storing multiple values in an array Casting and converting between types Handling exceptions Checking for overflow Practicing and exploring Summary
Writing, Debugging, and Testing Functions Chevron down icon Chevron up icon
Writing, Debugging, and Testing Functions Writing functions Debugging during development Hot reloading during development Unit testing Throwing and catching exceptions in functions Practicing and exploring Summary
Building Your Own Types with Object-Oriented Programming Chevron down icon Chevron up icon
Building Your Own Types with Object-Oriented Programming Talking about OOP Building class libraries Storing data in fields Working with methods and tuples Controlling access with properties and indexers Pattern matching with objects Working with record types Practicing and exploring Summary
Implementing Interfaces and Inheriting Classes Chevron down icon Chevron up icon
Implementing Interfaces and Inheriting Classes Setting up a class library and console application Static methods and overloading operators Making types safely reusable with generics Raising and handling events Implementing interfaces Working with null values Inheriting from classes Casting within inheritance hierarchies Inheriting and extending .NET types Summarizing custom type choices Practicing and exploring Summary
Packaging and Distributing .NET Types Chevron down icon Chevron up icon
Packaging and Distributing .NET Types The road to .NET 9 Understanding .NET components Publishing your code for deployment Native ahead-of-time compilation Packaging your libraries for NuGet distribution Working with preview features Practicing and exploring Summary
Working with Common .NET Types Chevron down icon Chevron up icon
Working with Common .NET Types Working with numbers Working with text Pattern matching with regular expressions Storing multiple objects in collections Working with spans, indexes, and ranges Practicing and exploring Summary
Working with Files, Streams, and Serialization Chevron down icon Chevron up icon
Working with Files, Streams, and Serialization Managing a filesystem Reading and writing with streams Encoding and decoding text Serializing object graphs Practicing and exploring Summary
Working with Data Using Entity Framework Core Chevron down icon Chevron up icon
Working with Data Using Entity Framework Core Understanding modern databases Setting up EF Core in a .NET project Defining EF Core models Querying EF Core models Practicing and exploring Summary
Querying and Manipulating Data Using LINQ Chevron down icon Chevron up icon
Querying and Manipulating Data Using LINQ Writing LINQ expressions LINQ in practice Sorting and more Using LINQ with EF Core Joining, grouping, and lookups Practicing and exploring Summary
Introducing Modern Web Development Using .NET Chevron down icon Chevron up icon
Introducing Modern Web Development Using .NET Understanding ASP.NET Core Structuring projects Building an entity model for use in the rest of the book Understanding web development Practicing and exploring Summary
Building Websites Using ASP.NET Core Chevron down icon Chevron up icon
Building Websites Using ASP.NET Core Exploring ASP.NET Core History of Blazor Exploring Blazor static SSR Using Entity Framework Core with ASP.NET Core Practicing and exploring Summary
Building Interactive Web Components Using Blazor Chevron down icon Chevron up icon
Building Interactive Web Components Using Blazor Reviewing the Blazor Web App project template Building components using Blazor Defining forms using the EditForm component Practicing and exploring Summary
Building and Consuming Web Services Chevron down icon Chevron up icon
Building and Consuming Web Services Building web services using ASP.NET Core Creating a web service for the Northwind database Documenting and trying out web services Consuming web services using HTTP clients Practicing and exploring Summary
Epilogue Chevron down icon Chevron up icon
Epilogue Next steps on your C# and .NET learning journey The tenth edition for .NET 10 Good luck! Leave a Review!
Index Chevron down icon Chevron up icon
Index
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Python Machine Learning By Example
Python Machine Learning By Example
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
By Yuxi (Hayden) Liu
July 2024 | 518 pages
Icon Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling
Icon Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions
Icon Implement ML models, such as neural networks and linear and logistic regression, from scratch
Icon Purchase of the print or Kindle book includes a free PDF copy
Getting Started with Machine Learning and Python Chevron down icon Chevron up icon
Getting Started with Machine Learning and Python An introduction to machine learning Knowing the prerequisites Getting started with three types of machine learning Digging into the core of machine learning Data preprocessing and feature engineering Combining models Installing software and setting up Summary Exercises Join our book’s Discord space
Building a Movie Recommendation Engine with Naïve Bayes Chevron down icon Chevron up icon
Building a Movie Recommendation Engine with Naïve Bayes Getting started with classification Exploring Naïve Bayes Implementing Naïve Bayes Building a movie recommender with Naïve Bayes Evaluating classification performance Tuning models with cross-validation Summary Exercises References Join our book’s Discord space
Predicting Online Ad Click-Through with Tree-Based Algorithms Chevron down icon Chevron up icon
Predicting Online Ad Click-Through with Tree-Based Algorithms A brief overview of ad click-through prediction Getting started with two types of data – numerical and categorical Exploring a decision tree from the root to the leaves Implementing a decision tree from scratch Implementing a decision tree with scikit-learn Predicting ad click-through with a decision tree Ensembling decision trees – random forests Ensembling decision trees – gradient-boosted trees Summary Exercises Join our book’s Discord space
Predicting Online Ad Click-Through with Logistic Regression Chevron down icon Chevron up icon
Predicting Online Ad Click-Through with Logistic Regression Converting categorical features to numerical – one-hot encoding and ordinal encoding Classifying data with logistic regression Training a logistic regression model Training on large datasets with online learning Handling multiclass classification Implementing logistic regression using TensorFlow Summary Exercises Join our book’s Discord space
Predicting Stock Prices with Regression Algorithms Chevron down icon Chevron up icon
Predicting Stock Prices with Regression Algorithms What is regression? Mining stock price data Getting started with feature engineering Estimating with linear regression Estimating with decision tree regression Implementing a regression forest Evaluating regression performance Predicting stock prices with the three regression algorithms Summary Exercises Join our book’s Discord space
Predicting Stock Prices with Artificial Neural Networks Chevron down icon Chevron up icon
Predicting Stock Prices with Artificial Neural Networks Demystifying neural networks Building neural networks Picking the right activation functions Preventing overfitting in neural networks Predicting stock prices with neural networks Summary Exercises Join our book’s Discord space
Mining the 20 Newsgroups Dataset with Text Analysis Techniques Chevron down icon Chevron up icon
Mining the 20 Newsgroups Dataset with Text Analysis Techniques How computers understand language – NLP Touring popular NLP libraries and picking up NLP basics Getting the newsgroups data Exploring the newsgroups data Thinking about features for text data Visualizing the newsgroups data with t-SNE Summary Exercises Join our book’s Discord space
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling Chevron down icon Chevron up icon
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling Learning without guidance – unsupervised learning Getting started with k-means clustering Clustering newsgroups dataset Discovering underlying topics in newsgroups Summary Exercises Join our book’s Discord space
Recognizing Faces with Support Vector Machine Chevron down icon Chevron up icon
Recognizing Faces with Support Vector Machine Finding the separating boundary with SVM Classifying face images with SVM Estimating with support vector regression Summary Exercises Join our book’s Discord space
Machine Learning Best Practices Chevron down icon Chevron up icon
Machine Learning Best Practices Machine learning solution workflow Best practices in the data preparation stage Best practices in the training set generation stage Best practices in the model training, evaluation, and selection stage Best practices in the deployment and monitoring stage Summary Exercises Join our book’s Discord space
Categorizing Images of Clothing with Convolutional Neural Networks Chevron down icon Chevron up icon
Categorizing Images of Clothing with Convolutional Neural Networks Getting started with CNN building blocks Architecting a CNN for classification Exploring the clothing image dataset Classifying clothing images with CNNs Boosting the CNN classifier with data augmentation Improving the clothing image classifier with data augmentation Advancing the CNN classifier with transfer learning Summary Exercises Join our book’s Discord space
Making Predictions with Sequences Using Recurrent Neural Networks Chevron down icon Chevron up icon
Making Predictions with Sequences Using Recurrent Neural Networks Introducing sequential learning Learning the RNN architecture by example Training an RNN model Overcoming long-term dependencies with LSTM Analyzing movie review sentiment with RNNs Revisiting stock price forecasting with LSTM Writing your own War and Peace with RNNs Summary Exercises Join our book’s Discord space
Advancing Language Understanding and Generation with the Transformer Models Chevron down icon Chevron up icon
Advancing Language Understanding and Generation with the Transformer Models Understanding self-attention Exploring the Transformer’s architecture Improving sentiment analysis with BERT and Transformers Generating text using GPT Summary Exercises Join our book’s Discord space
Building an Image Search Engine Using CLIP: a Multimodal Approach Chevron down icon Chevron up icon
Building an Image Search Engine Using CLIP: a Multimodal Approach Introducing the CLIP model Getting started with the dataset Finding images with words Summary Exercises References Join our book’s Discord space
Making Decisions in Complex Environments with Reinforcement Learning Chevron down icon Chevron up icon
Making Decisions in Complex Environments with Reinforcement Learning Setting up the working environment Introducing OpenAI Gym and Gymnasium Introducing reinforcement learning with examples Solving the FrozenLake environment with dynamic programming Performing Monte Carlo learning Solving the Blackjack problem with the Q-learning algorithm Summary Exercises Join our book’s Discord space
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
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Python for Algorithmic Trading Cookbook
Python for Algorithmic Trading Cookbook
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
By Jason Strimpel
August 2024 | 404 pages
Icon Follow practical Python recipes to acquire, visualize, and store market data for market research
Icon Design, backtest, and evaluate the performance of trading strategies using professional techniques
Icon Deploy trading strategies built in Python to a live trading environment with API connectivity
Icon Purchase of the print or Kindle book includes a free PDF eBook
Chapter 1: Acquire Free Financial Market Data with Cutting-Edge Python Libraries Chevron down icon Chevron up icon
Chapter 1: Acquire Free Financial Market Data with Cutting-Edge Python Libraries Technical requirements Working with stock market data with the OpenBB Platform Fetching historic futures data with the OpenBB Platform Navigating options market data with the OpenBB Platform Harnessing factor data using pandas_datareader
Chapter 2: Analyze and Transform Financial Market Data with pandas Chevron down icon Chevron up icon
Chapter 2: Analyze and Transform Financial Market Data with pandas Diving into pandas index types Building pandas Series and DataFrames Manipulating and transforming DataFrames Examining and selecting data from DataFrames Calculating asset returns using pandas Measuring the volatility of a return series Generating a cumulative return series Resampling data for different time frames Addressing missing data issues Applying custom functions to analyze time series data
Chapter 3: Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash Chevron down icon Chevron up icon
Chapter 3: Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash Quickly visualizing data using pandas Animating the evolution of the yield curve with Matplotlib Plotting options implied volatility surfaces with Matplotlib Visualizing statistical relationships with Seaborn Creating an interactive PCA analytics dashboard with Plotly Dash
Chapter 4: Store Financial Market Data on Your Computer Chevron down icon Chevron up icon
Chapter 4: Store Financial Market Data on Your Computer Storing data on disk in CSV format Storing data on disk with SQLite Storing data in a PostgreSQL database server Storing data in ultra-fast HDF5 format
Chapter 5: Build Alpha Factors for Stock Portfolios Chevron down icon Chevron up icon
Chapter 5: Build Alpha Factors for Stock Portfolios Identifying latent return drivers using principal component analysis Finding and hedging portfolio beta using linear regression Analyzing portfolio sensitivities to the Fama-French factors Assessing market inefficiency based on volatility Preparing a factor ranking model using Zipline Pipelines
Chapter 6: Vector-Based Backtesting with VectorBT Chevron down icon Chevron up icon
Chapter 6: Vector-Based Backtesting with VectorBT Building technical strategies with VectorBT Conducting walk-forward optimization with VectorBT Optimizing the SuperTrend strategy with VectorBT Pro
Chapter 7: Event-Based Backtesting Factor Portfolios with Zipline Reloaded Chevron down icon Chevron up icon
Chapter 7: Event-Based Backtesting Factor Portfolios with Zipline Reloaded Technical Requirements Backtesting a momentum factor strategy with Zipline Reloaded Exploring a mean reversion strategy with Zipline Reloaded
Chapter 8: Evaluate Factor Risk and Performance with Alphalens Reloaded Chevron down icon Chevron up icon
Chapter 8: Evaluate Factor Risk and Performance with Alphalens Reloaded Preparing backtest results Evaluating the information coefficient Examining factor return performance Evaluating factor turnover
Chapter 9: Assess Backtest Risk and Performance Metrics with Pyfolio Chevron down icon Chevron up icon
Chapter 9: Assess Backtest Risk and Performance Metrics with Pyfolio Preparing Zipline backtest results for Pyfolio Reloaded Generating strategy performance and return analytics Building a drawdown and rolling risk analysis Analyzing strategy holdings, leverage, exposure, and sector allocations Breaking Down Strategy Performance to Trade Level
Chapter 10: Set Up the Interactive Brokers Python API Chevron down icon Chevron up icon
Chapter 10: Set Up the Interactive Brokers Python API Building an algorithmic trading app Creating a Contract object with the IB API Creating an Order object with the IB API Fetching historical market data Getting a market data snapshot Streaming live market data Storing live tick data in a local SQL database
Chapter 11: Manage Orders, Positions, and Portfolios with the IB API Chevron down icon Chevron up icon
Chapter 11: Manage Orders, Positions, and Portfolios with the IB API Executing orders with the IB API Managing orders once they’re placed Getting details about your portfolio Inspecting positions and position details Computing portfolio profit and loss
Chapter 12: Deploy Strategies to a Live Environment Chevron down icon Chevron up icon
Chapter 12: Deploy Strategies to a Live Environment Calculating real-time key performance and risk indicators Sending orders based on portfolio targets Deploying a monthly factor portfolio strategy Deploying an options combo strategy Deploying an intraday multi-asset mean reversion strategy
Chapter 13: Advanced Recipes for Market Data and Strategy Management Chevron down icon Chevron up icon
Chapter 13: Advanced Recipes for Market Data and Strategy Management Streaming real-time options data with ThetaData Using the ArcticDB DataFrame database for tick storage Triggering real-time risk limit alerts Storing trade execution details in a SQL database
Index Chevron down icon Chevron up icon
Index Why subscribe?
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Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
By Sebastian Raschka
February 2022 | 774 pages
Icon Learn applied machine learning with a solid foundation in theory
Icon Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
Icon Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices
Giving Computers the Ability to Learn from Data Chevron down icon Chevron up icon
Giving Computers the Ability to Learn from Data Building intelligent machines to transform data into knowledge The three different types of machine learning Introduction to the basic terminology and notations A roadmap for building machine learning systems Using Python for machine learning Summary
Training Simple Machine Learning Algorithms for Classification Chevron down icon Chevron up icon
Training Simple Machine Learning Algorithms for Classification Artificial neurons – a brief glimpse into the early history of machine learning Implementing a perceptron learning algorithm in Python Adaptive linear neurons and the convergence of learning Summary
A Tour of Machine Learning Classifiers Using Scikit-Learn Chevron down icon Chevron up icon
A Tour of Machine Learning Classifiers Using Scikit-Learn Choosing a classification algorithm First steps with scikit-learn – training a perceptron Modeling class probabilities via logistic regression Maximum margin classification with support vector machines Solving nonlinear problems using a kernel SVM Decision tree learning K-nearest neighbors – a lazy learning algorithm Summary
Building Good Training Datasets – Data Preprocessing Chevron down icon Chevron up icon
Building Good Training Datasets – Data Preprocessing Dealing with missing data Handling categorical data Partitioning a dataset into separate training and test datasets Bringing features onto the same scale Selecting meaningful features Assessing feature importance with random forests Summary
Compressing Data via Dimensionality Reduction Chevron down icon Chevron up icon
Compressing Data via Dimensionality Reduction Unsupervised dimensionality reduction via principal component analysis Supervised data compression via linear discriminant analysis Nonlinear dimensionality reduction and visualization Summary
Learning Best Practices for Model Evaluation and Hyperparameter Tuning Chevron down icon Chevron up icon
Learning Best Practices for Model Evaluation and Hyperparameter Tuning Streamlining workflows with pipelines Using k-fold cross-validation to assess model performance Debugging algorithms with learning and validation curves Fine-tuning machine learning models via grid search Looking at different performance evaluation metrics Summary
Combining Different Models for Ensemble Learning Chevron down icon Chevron up icon
Combining Different Models for Ensemble Learning Learning with ensembles Combining classifiers via majority vote Bagging – building an ensemble of classifiers from bootstrap samples Leveraging weak learners via adaptive boosting Gradient boosting – training an ensemble based on loss gradients Summary
Applying Machine Learning to Sentiment Analysis Chevron down icon Chevron up icon
Applying Machine Learning to Sentiment Analysis Preparing the IMDb movie review data for text processing Introducing the bag-of-words model Training a logistic regression model for document classification Working with bigger data – online algorithms and out-of-core learning Topic modeling with latent Dirichlet allocation Summary
Predicting Continuous Target Variables with Regression Analysis Chevron down icon Chevron up icon
Predicting Continuous Target Variables with Regression Analysis Introducing linear regression Exploring the Ames Housing dataset Implementing an ordinary least squares linear regression model Fitting a robust regression model using RANSAC Evaluating the performance of linear regression models Using regularized methods for regression Turning a linear regression model into a curve – polynomial regression Dealing with nonlinear relationships using random forests Summary
Working with Unlabeled Data – Clustering Analysis Chevron down icon Chevron up icon
Working with Unlabeled Data – Clustering Analysis Grouping objects by similarity using k-means Organizing clusters as a hierarchical tree Locating regions of high density via DBSCAN Summary
Implementing a Multilayer Artificial Neural Network from Scratch Chevron down icon Chevron up icon
Implementing a Multilayer Artificial Neural Network from Scratch Modeling complex functions with artificial neural networks Classifying handwritten digits Training an artificial neural network About convergence in neural networks A few last words about the neural network implementation Summary
Parallelizing Neural Network Training with PyTorch Chevron down icon Chevron up icon
Parallelizing Neural Network Training with PyTorch PyTorch and training performance First steps with PyTorch Building input pipelines in PyTorch Building an NN model in PyTorch Choosing activation functions for multilayer neural networks Summary
Going Deeper – The Mechanics of PyTorch Chevron down icon Chevron up icon
Going Deeper – The Mechanics of PyTorch The key features of PyTorch PyTorch’s computation graphs PyTorch tensor objects for storing and updating model parameters Computing gradients via automatic differentiation Simplifying implementations of common architectures via the torch.nn module Project one – predicting the fuel efficiency of a car Project two – classifying MNIST handwritten digits Higher-level PyTorch APIs: a short introduction to PyTorch-Lightning Summary
Classifying Images with Deep Convolutional Neural Networks Chevron down icon Chevron up icon
Classifying Images with Deep Convolutional Neural Networks The building blocks of CNNs Putting everything together – implementing a CNN Implementing a deep CNN using PyTorch Smile classification from face images using a CNN Summary
Modeling Sequential Data Using Recurrent Neural Networks Chevron down icon Chevron up icon
Modeling Sequential Data Using Recurrent Neural Networks Introducing sequential data RNNs for modeling sequences Implementing RNNs for sequence modeling in PyTorch Summary
Transformers – Improving Natural Language Processing with Attention Mechanisms Chevron down icon Chevron up icon
Transformers – Improving Natural Language Processing with Attention Mechanisms Adding an attention mechanism to RNNs Introducing the self-attention mechanism Attention is all we need: introducing the original transformer architecture Building large-scale language models by leveraging unlabeled data Fine-tuning a BERT model in PyTorch Summary
Generative Adversarial Networks for Synthesizing New Data Chevron down icon Chevron up icon
Generative Adversarial Networks for Synthesizing New Data Introducing generative adversarial networks Implementing a GAN from scratch Improving the quality of synthesized images using a convolutional and Wasserstein GAN Other GAN applications Summary
Graph Neural Networks for Capturing Dependencies in Graph Structured Data Chevron down icon Chevron up icon
Graph Neural Networks for Capturing Dependencies in Graph Structured Data Introduction to graph data Understanding graph convolutions Implementing a GNN in PyTorch from scratch Implementing a GNN using the PyTorch Geometric library Other GNN layers and recent developments Summary
Reinforcement Learning for Decision Making in Complex Environments Chevron down icon Chevron up icon
Reinforcement Learning for Decision Making in Complex Environments Introduction – learning from experience The theoretical foundations of RL Reinforcement learning algorithms Implementing our first RL algorithm A glance at deep Q-learning Chapter and book summary
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
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Solutions Architect's Handbook
Solutions Architect's Handbook
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
By Saurabh Shrivastava
March 2024 | 578 pages
Icon Hits all the key areas -Rajesh Sheth, VP, Elastic Block Store, AWS
Icon Offers the knowledge you need to succeed in the evolving landscape of tech architecture - Luis Lopez Soria, Senior Specialist Solutions Architect, Google
Icon A valuable resource for enterprise strategists looking to build resilient applications - Cher Simon, Principal Solutions Architect, AWS
Solutions Architects in Organizations Chevron down icon Chevron up icon
Solutions Architects in Organizations What is solution architecture? The solutions architect’s role Understanding a solutions architect’s responsibilities Solutions architect in an Agile organization Common challenges in the solutions architect role Career path and skill development for solutions architects Summary
Principles of Solution Architecture Design Chevron down icon Chevron up icon
Principles of Solution Architecture Design Building scalable architecture design Building a highly available and resilient architecture Making your architecture fault-tolerant Designing for performance Creating immutable architecture Think loose coupling Think service, not server Think data-driven design Adding security everywhere Making applications usable and accessible Building future-proof extendable and reusable architecture Ensuring architectural interoperability and portability Applying automation everywhere Plan for business continuity Design for operation Overcoming architectural constraints Summary
Cloud Migration and Cloud Architecture Design Chevron down icon Chevron up icon
Cloud Migration and Cloud Architecture Design Public, private, and hybrid clouds Solution architecture in the public cloud Creating a cloud migration strategy Choosing a cloud migration strategy Steps for cloud migration Creating a hybrid cloud architecture Taking a multi-cloud approach Implementing CloudOps CloudOps pillars Summary Further reading
Solution Architecture Design Patterns Chevron down icon Chevron up icon
Solution Architecture Design Patterns Building an n-tier layered architecture Creating a multi-tenant SaaS-based architecture Understanding service-oriented architecture Building a cache-based architecture Model-View-Controller (MVC) architecture Building Domain-Driven Design (DDD) Understanding the circuit breaker pattern Implementing the bulkhead pattern Creating a floating IP pattern Deploying an application with a container Database handling in application architecture Clean Architecture Avoiding anti-patterns in solution architecture Summary
Cloud-Native Architecture Design Patterns Chevron down icon Chevron up icon
Cloud-Native Architecture Design Patterns What is cloud-native architecture? Building serverless architecture Building stateless and stateful architectural designs Creating a microservice architecture Reactive architecture Building queue-based architecture Pipes-and-Filters Architecture Creating Event-Driven Architecture Backend for Frontend pattern Cloud-native architecture anti-patterns Summary
Performance Considerations Chevron down icon Chevron up icon
Performance Considerations Design principles for high-performance architecture Technology selection for performance optimization Performance considerations for mobile applications Performance testing Managing performance monitoring Summary
Security Considerations Chevron down icon Chevron up icon
Security Considerations Design principles for architectural security Selecting technology for architectural security Security and compliance certifications The cloud’s shared security responsibility model Securing threat modeling Summary
Architectural Reliability Considerations Chevron down icon Chevron up icon
Architectural Reliability Considerations Design principles for architectural reliability Technology selection for architectural reliability Improving reliability with the cloud Summary
Operational Excellence Considerations Chevron down icon Chevron up icon
Operational Excellence Considerations Design principles for operational excellence Selecting technologies for operational excellence Achieving operational excellence in the public cloud Driving efficiency with CloudOps Summary
Cost Considerations Chevron down icon Chevron up icon
Cost Considerations Design principles for cost optimization Understanding techniques for cost optimization Driving cost optimization in the public cloud Green IT and its influence on cost considerations Summary
DevOps and Solution Architecture Framework Chevron down icon Chevron up icon
DevOps and Solution Architecture Framework Introducing DevOps Understanding the components of DevOps Introducing DevSecOps for Security Combining DevSecOps and CI/CD Implementing a CD strategy Best practices for choosing the right deployment strategy Implementing continuous testing in the CI/CD pipeline Using DevOps tools for CI/CD Implementing DevOps best practices Building DevOps and DevSecOps in the cloud Summary
Data Engineering for Solution Architecture Chevron down icon Chevron up icon
Data Engineering for Solution Architecture What is big data architecture? Designing big data processing pipelines Data ingestion, storage, processing, and analytics Data storage in the cloud Visualizing data Designing big data architectures Big data architecture best practices Summary
Machine Learning Architecture Chevron down icon Chevron up icon
Machine Learning Architecture What is machine learning? Working with data science and machine learning Machine learning in the cloud Building machine learning architecture Design principles for machine learning architecture MLOps Deep learning NLP Summary
Generative AI Architecture Chevron down icon Chevron up icon
Generative AI Architecture What is generative AI? Generative AI use cases The basic architecture of generative AI systems Popular generative AI FMs How to start with generative AI Generative AI reference architecture for building a mortgage assistant app Challenges in implementing generative AI Summary
Rearchitecting Legacy Systems Chevron down icon Chevron up icon
Rearchitecting Legacy Systems Learning the challenges of legacy systems Defining a strategy for system modernization Looking at legacy system modernization techniques Defining a cloud migration strategy for legacy systems Mainframe migration with the public cloud Modernizing legacy code with generative AI Summary
Solution Architecture Document Chevron down icon Chevron up icon
Solution Architecture Document Purpose of the SAD Views of the SAD Structure of the SAD Life cycle of the SAD SAD best practices and common pitfalls IT procurement documentation for a solution architecture Summary
Learning Soft Skills to Become a Better Solutions Architect Chevron down icon Chevron up icon
Learning Soft Skills to Become a Better Solutions Architect Importance of soft skills in solution architecture Acquiring pre-sales skills Taking ownership and accountability Being flexible and adaptable Design thinking Being a builder by engaging in coding hands-on Becoming better with continuous learning Being a mentor to others Becoming a technology evangelist and thought leader Summary
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
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RAG-Driven Generative AI
RAG-Driven Generative AI
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
By Denis Rothman
September 2024 | 334 pages
Icon Implement RAG’s traceable outputs, linking each response to its source document to build reliable multimodal conversational agents
Icon Deliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphs
Icon Balance cost and performance between dynamic retrieval datasets and fine-tuning static data
Why Retrieval Augmented Generation? Chevron down icon Chevron up icon
Why Retrieval Augmented Generation? What is RAG? Naïve, advanced, and modular RAG configurations RAG versus fine-tuning The RAG ecosystem Naïve, advanced, and modular RAG in code Summary Questions References Further reading
RAG Embedding Vector Stores with Deep Lake and OpenAI Chevron down icon Chevron up icon
RAG Embedding Vector Stores with Deep Lake and OpenAI From raw data to embeddings in vector stores Organizing RAG in a pipeline A RAG-driven generative AI pipeline Building a RAG pipeline Evaluating the output with cosine similarity Summary Questions References Further reading
Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI Chevron down icon Chevron up icon
Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI Why use index-based RAG? Building a semantic search engine and generative agent for drone technology Vector store index query engine Tree index query engine List index query engine Keyword index query engine Summary Questions References Further reading
Multimodal Modular RAG for Drone Technology Chevron down icon Chevron up icon
Multimodal Modular RAG for Drone Technology What is multimodal modular RAG? Building a multimodal modular RAG program for drone technology Summary Questions References Further reading
Boosting RAG Performance with Expert Human Feedback Chevron down icon Chevron up icon
Boosting RAG Performance with Expert Human Feedback Adaptive RAG Building hybrid adaptive RAG in Python Summary Questions References Further reading
Scaling RAG Bank Customer Data with Pinecone Chevron down icon Chevron up icon
Scaling RAG Bank Customer Data with Pinecone Scaling with Pinecone Pipeline 1: Collecting and preparing the dataset Pipeline 2: Scaling a Pinecone index (vector store) Pipeline 3: RAG generative AI Summary Questions References Further reading
Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex Chevron down icon Chevron up icon
Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex The architecture of RAG for knowledge-graph-based semantic search Pipeline 1: Collecting and preparing the documents Pipeline 2: Creating and populating the Deep Lake vector store Pipeline 3: Knowledge graph index-based RAG Summary Questions References Further reading
Dynamic RAG with Chroma and Hugging Face Llama Chevron down icon Chevron up icon
Dynamic RAG with Chroma and Hugging Face Llama The architecture of dynamic RAG Installing the environment Activating session time Downloading and preparing the dataset Embedding and upserting the data in a Chroma collection Querying the collection Prompt and retrieval RAG with Llama Total session time Summary Questions References Further reading
Empowering AI Models: Fine-Tuning RAG Data and Human Feedback Chevron down icon Chevron up icon
Empowering AI Models: Fine-Tuning RAG Data and Human Feedback The architecture of fine-tuning static RAG data Installing the environment 1. Preparing the dataset for fine-tuning 2. Fine-tuning the model 3. Using the fine-tuned OpenAI model Metrics Summary Questions References Further reading
RAG for Video Stock Production with Pinecone and OpenAI Chevron down icon Chevron up icon
RAG for Video Stock Production with Pinecone and OpenAI The architecture of RAG for video production The environment of the video production ecosystem Pipeline 1: Generator and Commentator Pipeline 2: The Vector Store Administrator Pipeline 3: The Video Expert Summary Questions References Further reading
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
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50 Algorithms Every Programmer Should Know
50 Algorithms Every Programmer Should Know
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
By Imran Ahmad
September 2023 | 538 pages
Icon Familiarize yourself with advanced deep learning architectures
Icon Explore newer topics, such as handling hidden bias in data and algorithm explainability
Icon Get to grips with different programming algorithms and choose the right data structures for their optimal implementation
Section 1: Fundamentals and Core Algorithms Chevron down icon Chevron up icon
Section 1: Fundamentals and Core Algorithms
Overview of Algorithms Chevron down icon Chevron up icon
Overview of Algorithms What is an algorithm? Python packages Algorithm design techniques Performance analysis Selecting an algorithm Validating an algorithm Summary
Data Structures Used in Algorithms Chevron down icon Chevron up icon
Data Structures Used in Algorithms Exploring Python built-in data types Exploring abstract data types Summary
Sorting and Searching Algorithms Chevron down icon Chevron up icon
Sorting and Searching Algorithms Introducing sorting algorithms Introduction to searching algorithms Practical applications Summary
Designing Algorithms Chevron down icon Chevron up icon
Designing Algorithms Introducing the basic concepts of designing an algorithm Understanding algorithmic strategies A practical application – solving the TSP Presenting the PageRank algorithm Understanding linear programming Summary
Graph Algorithms Chevron down icon Chevron up icon
Graph Algorithms Understanding graphs: a brief introduction Graph theory and network analysis Representations of graphs Graph mechanics and types Introducing network analysis theory Understanding graph traversals Case study: fraud detection using SNA Summary
Section 2: Machine Learning Algorithms Chevron down icon Chevron up icon
Section 2: Machine Learning Algorithms
Unsupervised Machine Learning Algorithms Chevron down icon Chevron up icon
Unsupervised Machine Learning Algorithms Introducing unsupervised learning Understanding clustering algorithms Steps of hierarchical clustering Coding a hierarchical clustering algorithm Understanding DBSCAN Creating clusters using DBSCAN in Python Evaluating the clusters Dimensionality reduction Association rules mining Summary
Traditional Supervised Learning Algorithms Chevron down icon Chevron up icon
Traditional Supervised Learning Algorithms Understanding supervised machine learning Formulating supervised machine learning problems Understanding classification algorithms Decision tree classification algorithm Understanding the ensemble methods Logistic regression The SVM algorithm Bayes’ theorem For classification algorithms, the winner is... Linear regression For regression algorithms, the winner is... Practical example – how to predict the weather Summary
Neural Network Algorithms Chevron down icon Chevron up icon
Neural Network Algorithms The evolution of neural networks Understanding neural networks Training a neural network Understanding the anatomy of a neural network Defining gradient descent Activation functions Tools and frameworks Choosing a sequential or functional model Understanding the types of neural networks Using transfer learning Case study – using deep learning for fraud detection Summary
Algorithms for Natural Language Processing Chevron down icon Chevron up icon
Algorithms for Natural Language Processing Introducing NLP Understanding NLP terminology Cleaning data using Python Understanding the Term Document Matrix Introduction to word embedding Implementing word embedding with Word2Vec Case study: Restaurant review sentiment analysis Applications of NLP Summary
Understanding Sequential Models Chevron down icon Chevron up icon
Understanding Sequential Models Understanding sequential data Data representation for sequential models Introducing RNNs GRU Introducing LSTM Summary
Advanced Sequential Modeling Algorithms Chevron down icon Chevron up icon
Advanced Sequential Modeling Algorithms The evolution of advanced sequential modeling techniques Exploring autoencoders Understanding the Seq2Seq model Understanding the attention mechanism Delving into self-attention Transformers: the evolution in neural networks after self-attention LLMs Bottom of Form Summary
Section 3: Advanced Topics Chevron down icon Chevron up icon
Section 3: Advanced Topics
Recommendation Engines Chevron down icon Chevron up icon
Recommendation Engines Introducing recommendation systems Types of recommendation engines Understanding the limitations of recommendation systems Areas of practical applications Practical example – creating a recommendation engine Summary
Algorithmic Strategies for Data Handling Chevron down icon Chevron up icon
Algorithmic Strategies for Data Handling Introduction to data algorithms Presenting the CAP theorem Decoding data compression algorithms Practical example: Data management in AWS: A focus on CAP theorem and compression algorithms Summary
Cryptography Chevron down icon Chevron up icon
Cryptography Introduction to cryptography Understanding the types of cryptographic techniques Example: security concerns when deploying a machine learning model Summary
Large-Scale Algorithms Chevron down icon Chevron up icon
Large-Scale Algorithms Introduction to large-scale algorithms Characterizing performant infrastructure for large-scale algorithms Strategizing multi-resource processing Understanding theoretical limitations of parallel computing How Apache Spark empowers large-scale algorithm processing Using large-scale algorithms in cloud computing Summary
Practical Considerations Chevron down icon Chevron up icon
Practical Considerations Challenges facing algorithmic solutions Failure of Tay, the Twitter AI bot The explainability of an algorithm Understanding ethics and algorithms Reducing bias in models When to use algorithms Summary
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Building LLM Powered Applications
Building LLM Powered Applications
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
By Valentina Alto
May 2024 | 342 pages
Icon Embed LLMs into real-world applications
Icon Use LangChain to orchestrate LLMs and their components within applications
Icon Grasp basic and advanced techniques of prompt engineering
Introduction to Large Language Models Chevron down icon Chevron up icon
Introduction to Large Language Models What are large foundation models and LLMs? Most popular LLM transformers-based architectures Training and evaluating LLMs Base models versus customized models Summary References
LLMs for AI-Powered Applications Chevron down icon Chevron up icon
LLMs for AI-Powered Applications How LLMs are changing software development The copilot system Introducing AI orchestrators to embed LLMs into applications Summary References
Choosing an LLM for Your Application Chevron down icon Chevron up icon
Choosing an LLM for Your Application The most promising LLMs in the market Beyond language models A decision framework to pick the right LLM Summary References
Prompt Engineering Chevron down icon Chevron up icon
Prompt Engineering Technical requirements What is prompt engineering? Principles of prompt engineering Advanced techniques Summary References
Embedding LLMs within Your Applications Chevron down icon Chevron up icon
Embedding LLMs within Your Applications Technical requirements A brief note about LangChain Getting started with LangChain Working with LLMs via the Hugging Face Hub Summary References
Building Conversational Applications Chevron down icon Chevron up icon
Building Conversational Applications Technical requirements Getting started with conversational applications Developing the front-end with Streamlit Summary References
Search and Recommendation Engines with LLMs Chevron down icon Chevron up icon
Search and Recommendation Engines with LLMs Technical requirements Introduction to recommendation systems Existing recommendation systems How LLMs are changing recommendation systems Implementing an LLM-powered recommendation system Developing the front-end with Streamlit Summary References
Using LLMs with Structured Data Chevron down icon Chevron up icon
Using LLMs with Structured Data Technical requirements What is structured data? Getting started with relational databases Implementing the DBCopilot with LangChain Developing the front-end with Streamlit Summary References
Working with Code Chevron down icon Chevron up icon
Working with Code Technical requirements Choosing the right LLM for code Code understanding and generation Act as an algorithm Leveraging Code Interpreter Summary References
Building Multimodal Applications with LLMs Chevron down icon Chevron up icon
Building Multimodal Applications with LLMs Technical requirements Why multimodality? Building a multimodal agent with LangChain Option 1: Using an out-of-the-box toolkit for Azure AI Services Option 2: Combining single tools into one agent Option 3: Hard-coded approach with a sequential chain Comparing the three options Developing the front-end with Streamlit Summary References
Fine-Tuning Large Language Models Chevron down icon Chevron up icon
Fine-Tuning Large Language Models Technical requirements What is fine-tuning? When is fine-tuning necessary? Getting started with fine-tuning Summary References
Responsible AI Chevron down icon Chevron up icon
Responsible AI What is Responsible AI and why do we need it? Responsible AI architecture Regulations surrounding Responsible AI Summary References
Emerging Trends and Innovations Chevron down icon Chevron up icon
Emerging Trends and Innovations The latest trends in language models and generative AI Companies embracing generative AI Summary References
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
Index Chevron down icon Chevron up icon
Index
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Learn Python Programming
Learn Python Programming
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By Fabrizio Romano
November 2024 | 616 pages
Icon Create and deploy APIs and CLI applications, leveraging Python’s strengths in scripting and automation
Icon Stay current with the latest features and improvements in Python, including pattern matching and the latest exception handling syntax
Icon Engage with new real-world examples and projects, including competitive programming problems, to solidify your understanding of Python
A Gentle Introduction to Python Chevron down icon Chevron up icon
A Gentle Introduction to Python A brief introduction to programming Enter the Python About Python What are the drawbacks? Who is using Python today? Setting up the environment How to run a Python program How is Python code organized? Python’s execution model Guidelines for writing good code Python culture A note on IDEs A word about AI Summary
Built-In Data Types Chevron down icon Chevron up icon
Built-In Data Types Everything is an object Mutability Numbers Immutable sequences Mutable sequences Set types Mapping types: dictionaries Data types Final considerations Summary
Conditionals and Iteration Chevron down icon Chevron up icon
Conditionals and Iteration Conditional programming Looping Assignment expressions Putting all this together A quick peek at the itertools module Summary
Functions, the Building Blocks of Code Chevron down icon Chevron up icon
Functions, the Building Blocks of Code Why use functions? Scopes and name resolution Input parameters Return values A few useful tips Recursive functions Anonymous functions Function attributes Built-in functions Documenting your code Importing objects One final example Summary
Comprehensions and Generators Chevron down icon Chevron up icon
Comprehensions and Generators The map, zip, and filter functions Comprehensions Generators Some performance considerations Do not overdo comprehensions and generators Name localization Generation behavior in built-ins One last example Summary
OOP, Decorators, and Iterators Chevron down icon Chevron up icon
OOP, Decorators, and Iterators Decorators OOP Writing a custom iterator Summary
Exceptions and Context Managers Chevron down icon Chevron up icon
Exceptions and Context Managers Exceptions Context managers Summary
Files and Data Persistence Chevron down icon Chevron up icon
Files and Data Persistence Working with files and directories Data interchange formats I/O, streams, and requests Persisting data on disk Configuration files Summary
Cryptography and Tokens Chevron down icon Chevron up icon
Cryptography and Tokens The need for cryptography Hashlib HMAC Secrets JSON Web Tokens Useful references Summary
Testing Chevron down icon Chevron up icon
Testing Testing your application Test-driven development Summary
Debugging and Profiling Chevron down icon Chevron up icon
Debugging and Profiling Debugging techniques Troubleshooting guidelines Profiling Python Summary
Introduction to Type Hinting Chevron down icon Chevron up icon
Introduction to Type Hinting Python approach to types History of type hinting Benefits of type hinting Type annotations The Mypy static type checker Some useful resources Summary
Data Science in Brief Chevron down icon Chevron up icon
Data Science in Brief IPython and Jupyter Notebook Dealing with data Where do we go from here? Summary
Introduction to API Development Chevron down icon Chevron up icon
Introduction to API Development The Hypertext Transfer Protocol APIs – An introduction The railway API Where do we go from here? Summary
CLI Applications Chevron down icon Chevron up icon
CLI Applications Command-line arguments Building a CLI client for the railway API Other resources and tools Summary
Packaging Python Applications Chevron down icon Chevron up icon
Packaging Python Applications The Python Package Index Packaging with Setuptools Building and publishing packages Advice for starting new projects Other files Alternative tools Further reading Summary
Programming Challenges Chevron down icon Chevron up icon
Programming Challenges Advent of Code Final considerations Other programming challenge websites Summary
Other Books You May Enjoy Chevron down icon Chevron up icon
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Index Chevron down icon Chevron up icon
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Learning Angular
Learning Angular
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By Aristeidis Bampakos
January 2025 | 450 pages
Icon Explore the basics of Angular development, from components and templates to forms, routing, and services
Icon Get up to speed with the new paradigms modern Angular brings, including standalone APIs, new control flow syntax, Signals, and server-side rendering (SSR)
Icon Discover best practices for building, deploying, debugging, and testing Angular applications
Learning Angular, Fifth Edition: A practical guide to building web applications with modern Angular Chevron down icon Chevron up icon
Learning Angular, Fifth Edition: A practical guide to building web applications with modern Angular
1 Building Your First Angular Application Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements What is Angular Why choose Angular Setting up the Angular CLI workspace Structure of an Angular application Angular tooling Summary
2 Introduction to TypeScript Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements JavaScript essentials What is TypeScript? Getting started with TypeScript Types Interfaces Decorators Modules Summary
3 Structuring User Interfaces with Components Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Creating our first component Interacting with the template Component inter-communication Encapsulating CSS styling Deciding on a change detection strategy Introducing the component lifecycle Summary
4 Enriching Applications Using Pipes and Directives Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Manipulating data with pipes Building pipes Introducing directives Building directives Summary
5 Managing Complex Tasks with Services Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Introducing the Angular DI Creating our first Angular service Providing dependencies across the application Injecting services in the component tree Overriding providers in the injector hierarchy Summary
6 Organizing Applications into Modules Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Introducing Angular modules Creating our first module Group application features into modules Leveraging Angular built-in modules Summary
7 Reactive Patterns in Angular Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Strategies for handling asynchronous information Reactive programming in Angular The RxJS library Subscribing to observables Unsubscribing from observables Tracking application state with signals Summary
8 Communicating with Data Services over HTTP Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Communicating data over HTTP Introducing the Angular HTTP client Setting up a backend API Handling CRUD data in Angular Authentication and authorization with HTTP Summary
9 Navigating through Applications with Routing Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Introducing the Angular router Configuring the main routes Organizing application routes Passing parameters to routes Enhancing navigation with advanced features Summary
10 Collecting User Data with Forms Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Introducing web forms Building template-driven forms Building reactive forms Validating input in forms Manipulating form state Summary
11 Handling Application Errors Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Handling different types of errors Demystifying framework errors Summary
12 Introduction to Angular Material Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Introducing Material Design Introducing Angular Material Integrating UI components Summary
13 Unit Testing Angular Applications Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Why do we need tests? The anatomy of a unit test Introducing unit tests in Angular Testing components Testing services Testing pipes Testing directives Testing forms Testing router Summary
14 Bringing Applications to Production Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Technical requirements Building an Angular application Limiting the application bundle size Optimizing the application bundle Deploying an Angular application Summary
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React Key Concepts
React Key Concepts
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By Maximilian Schwarzmüller
November 2024 | 0 pages
Icon Work through clear, concise explanations of core React 19 functionalities
Icon Complete practical exercises that challenge you to build your own simple apps
Icon Discover fullstack React with Next.js, React Server Components, Suspense, and more
React Key Concepts, Second Edition: An in-depth guide to React’s core features Chevron down icon Chevron up icon
React Key Concepts, Second Edition: An in-depth guide to React’s core features
React – What and Why Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction What Is React? The Problem with "Vanilla JavaScript" React and Declarative Code Introducing Single Page Applications Summary and Key Takeaways
Understanding React Components and JSX Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction What Are Components? What Does React Do with All These Components? JSX vs HTML vs Vanilla JavaScript Moving Beyond Static Content When Should You Split Components? Summary and Key Takeaways Apply What You Learned
Components and Props Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction Not There Yet Using Props in Components Components, Props, and Reusability How to Deal with Multiple Props Summary and Key Takeaways Apply What You Learned
Working with Events and State Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction What's the Problem? Updating State Correctly A Look under the Hood of React Working with Multiple State Values Deriving Values from State Summary and Key Takeaways Apply What You Learned
Rendering Lists and Conditional Content Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction What Are Conditional Content and List Data? Rendering Content Conditionally Outputting List Data A Problem with List Items Summary and Key Takeaways Apply What You Learned
Styling React Apps Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction How Does Styling Work in React Apps? Scoped Styles with CSS Modules Summary and Key Takeaways Apply What You Learned
Portals and Refs Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction A World without Refs Refs versus State Using Refs for More than DOM Access React and Where Things End up in the DOM Summary and Key Takeaways Apply What You Have Learned
Handling Side Effects Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction What's the Problem? Understanding Side Effects Dealing with Side Effects with the useEffect() Hook Effects and Their Dependencies Summary and Key Takeaways Apply What You Learned
Behind the Scenes of React and Optimization Opportunities Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction Revisiting Component Evaluations and Updates The Virtual DOM vs the Real DOM Avoiding Unnecessary Code Download Strict Mode Debugging Code and the React Developer Tools Summary and Key Takeaways Apply What You Learned
Working with Complex State Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction A Problem with Cross-Component State Using Context to Handle Multi-Component State Limitations of useState( ) Managing State with useReducer( ) Summary and Key Takeaways Apply What You Learned
Building Custom React Hooks Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction Why Would You Build Custom Hooks? Custom Hooks: A Flexible Feature A More Complex Example Summary and Key Takeaways Apply What You Learned
Multipage Apps with React Router Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction One Page Is Not Enough Getting Started with React Router and Defining Routes From Static to Dynamic Routes Redirecting Summary and Key Takeaways Apply What You Learned
Managing Data with React Router Chevron down icon Chevron up icon
Before you begin: Join our book community on Discord Introduction Data Fetching and Routing Are Tightly Coupled Loading Data with React Router Getting Access to Loaded Data Layouts Revisited Handling Errors Onward to Data Submission Summary and Key Takeaways Apply What You Learned
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Real-World Web Development with .NET 9
Real-World Web Development with .NET 9
Read now
By Mark J. Price
December 2024 | 0 pages
Icon Master ASP.NET Core MVC, Web API, and OData for building robust web services.
Icon Get hands-on experience with web testing, security, and containerization techniques.
Icon Learn how to implement Umbraco CMS for content management websites.
Real-World Web Development with .NET 9: Build websites and services using mature and proven ASP.NET Core MVC, Web API, and Umbraco CMS Chevron down icon Chevron up icon
Real-World Web Development with .NET 9: Build websites and services using mature and proven ASP.NET Core MVC, Web API, and Umbraco CMS
1 Introducing Web Development Using Controllers Chevron down icon Chevron up icon
Join our book community on Discord Understanding ASP.NET Core Structuring projects and managing packages Making good use of the GitHub repository for this book Building an entity model for use in the rest of the book Practicing and exploring Summary
2 Building Websites Using ASP.NET Core MVC Chevron down icon Chevron up icon
Join our book community on Discord Setting up an ASP.NET Core MVC website Exploring an ASP.NET Core MVC website Prototyping with Bootstrap Customizing an ASP.NET Core MVC website Practicing and exploring Summary
3 Model Binding, Validation, and Data Using EF Core Chevron down icon Chevron up icon
Join our book community on Discord Model binding and validation Modifying data using EF Core and ASP.NET Core Improving scalability using asynchronous tasks Practicing and exploring Summary
4 Building and Localizing Web User Interfaces Chevron down icon Chevron up icon
Join our book community on Discord Defining web user interfaces with Razor views Defining web user interfaces with Tag Helpers Localizing web user interfaces with ASP.NET Core Practicing and exploring Summary
5 Authentication and Authorization Chevron down icon Chevron up icon
Join our book community on Discord Introducing authentication and authorization Securing controller action methods using filters Practicing and exploring Summary
6 Performance Optimization Using Caching Chevron down icon Chevron up icon
Join our book community on Discord Introducing caching with ASP.NET Core Output caching Object caching More techniques to improve scalability Practicing and exploring Summary
7 Web User Interface Testing Using Playwright Chevron down icon Chevron up icon
Join our book community on Discord Introducing web user interface testing Testing web user interfaces using Playwright Interacting with a web user interface Generating tests with the Playwright Inspector Practicing and exploring Summary
8 Configuring and Containerizing ASP.NET Core Projects Chevron down icon Chevron up icon
Join our book community on Discord Configuring dependency services Configuring the HTTP pipeline Configuring options Containerizing ASP.NET Core projects Practicing and exploring Summary
9 Building Web Services Using ASP.NET Core Web API Chevron down icon Chevron up icon
Join our book community on Discord Introducing web services Creating a web service for the Northwind database Documenting and trying out web services Caching and logging Consuming web services using HTTP clients Relaxing the same origin security policy using CORS Understanding identity services Practicing and exploring Summary
10 Building Web Services Using ASP.NET Core OData Chevron down icon Chevron up icon
Join our book community on Discord Understanding OData Building a web service that supports OData Exploring OData services using HTTP/REST tools Implementing versions and data modifications Building clients for OData services Practicing and exploring Summary
11 Building Web Services Using FastEndpoints Chevron down icon Chevron up icon
Join our book community on Discord Introducing FastEndpoints Implementing FastEndpoints Configuring FastEndpoints Practicing and exploring Summary
12 Web Service Integration Testing Chevron down icon Chevron up icon
Join our book community on Discord Basics of integration testing Integration testing with data stores Testing web services using xUnit Mocking in tests Testing services using dev tunnels Practicing and exploring Summary
13 Web Content Management Using Umbraco Chevron down icon Chevron up icon
Join our book community on Discord Understanding the benefits of a CMS Introducing Umbraco CMS Defining document types Working with media Practicing and exploring Summary
14 Customizing and Extending Umbraco Chevron down icon Chevron up icon
Join our book community on Discord Umbraco settings Working with Views and Razor Syntax UmbracoHelper class Practicing and exploring Summary
15 Epilogue Chevron down icon Chevron up icon
Join our book community on Discord Next steps on your web development learning journey The next edition for .NET 10 Good luck!
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Unlocking Data with Generative AI and RAG
Unlocking Data with Generative AI and RAG
Read now
By Keith Bourne
September 2024 | 346 pages
Icon Optimize data retrieval and generation using vector databases
Icon Boost decision-making and automate workflows with AI agents
Icon Overcome common challenges in implementing real-world RAG systems
Icon Purchase of the print or Kindle book includes a free PDF eBook
Part 1 – Introduction to Retrieval-Augmented Generation (RAG) Chevron down icon Chevron up icon
Part 1 – Introduction to Retrieval-Augmented Generation (RAG)
Chapter 1: What Is Retrieval-Augmented Generation (RAG) Chevron down icon Chevron up icon
Chapter 1: What Is Retrieval-Augmented Generation (RAG) Understanding RAG – Basics and principles RAG vocabulary Vectors Implementing RAG in AI applications Comparing RAG with conventional generative AI Comparing RAG with model fine-tuning The architecture of RAG systems Summary
Chapter 2: Code Lab – An Entire RAG Pipeline Chevron down icon Chevron up icon
Chapter 2: Code Lab – An Entire RAG Pipeline Technical requirements No interface! Setting up a large language model (LLM) account Installing the necessary packages Indexing Retrieval and generation Submitting a question for RAG Final output Complete code Summary
Chapter 3: Practical Applications of RAG Chevron down icon Chevron up icon
Chapter 3: Practical Applications of RAG Technical requirements Customer support and chatbots with RAG RAG for automated reporting E-commerce support Utilizing knowledge bases with RAG Innovation scouting and trend analysis Leveraging RAG for personalized recommendations in marketing communications Training and education Code lab 3.1 – Adding sources to your RAG Summary
Chapter 4: Components of a RAG System Chevron down icon Chevron up icon
Chapter 4: Components of a RAG System Technical requirements Key component overview Indexing Retrieval and generation Prompting Defining your LLM UI Evaluation Summary References
Chapter 5: Managing Security in RAG Applications Chevron down icon Chevron up icon
Chapter 5: Managing Security in RAG Applications Technical requirements How RAG can be leveraged as a security solution RAG security challenges Red teaming Common areas to target with red teaming Code lab 5.1 – Securing your keys Code lab 5.2 – Red team attack! Code lab 5.3 – Blue team defend! Summary
Part 2 – Components of RAG Chevron down icon Chevron up icon
Part 2 – Components of RAG
Chapter 6: Interfacing with RAG and Gradio Chevron down icon Chevron up icon
Chapter 6: Interfacing with RAG and Gradio Technical requirements Why Gradio? Benefits of using Gradio Limitations to using Gradio Code lab – Adding a Gradio interface Summary
Chapter 7: The Key Role Vectors and Vector Stores Play in RAG Chevron down icon Chevron up icon
Chapter 7: The Key Role Vectors and Vector Stores Play in RAG Technical requirements Fundamentals of vectors in RAG Where vectors lurk in your code The amount of text you vectorize matters! Not all semantics are created equal! Code lab 7.1 – Common vectorization techniques Factors in selecting a vectorization option Getting started with vector stores Choosing a vector store Summary
Chapter 8: Similarity Searching with Vectors Chevron down icon Chevron up icon
Chapter 8: Similarity Searching with Vectors Technical requirements Distance metrics versus similarity algorithms versus vector search Vector space Semantic versus keyword search Code lab 8.1 – Semantic distance metrics Different search paradigms – sparse, dense, and hybrid Code lab 8.2 – Hybrid search with a custom function Code lab 8.3 – Hybrid search with LangChain’s EnsembleRetriever to replace our custom function Semantic search algorithms Enhancing search with indexing techniques Vector search options Summary
Chapter 9: Evaluating RAG Quantitatively and with Visualizations Chevron down icon Chevron up icon
Chapter 9: Evaluating RAG Quantitatively and with Visualizations Technical requirements Evaluate as you build Evaluate after you deploy Evaluation helps you get better Standardized evaluation frameworks What is the ground truth? Code lab 9.1 – ragas End-to-end evaluation Other component-wise evaluation Additional evaluation techniques Summary References
Chapter 10: Key RAG Components in LangChain Chevron down icon Chevron up icon
Chapter 10: Key RAG Components in LangChain Technical requirements Code lab 10.1 – LangChain vector store Code lab 10.2 – LangChain Retrievers Code lab 10.3 – LangChain LLMs Summary
Chapter 11: Using LangChain to Get More from RAG Chevron down icon Chevron up icon
Chapter 11: Using LangChain to Get More from RAG Technical requirements Code lab 11.1 – Document loaders Code lab 11.2 – Text splitters Code lab 11.3 – Output parsers Summary
Part 3 – Implementing Advanced RAG Chevron down icon Chevron up icon
Part 3 – Implementing Advanced RAG
Chapter 12: Combining RAG with the Power of AI Agents and LangGraph Chevron down icon Chevron up icon
Chapter 12: Combining RAG with the Power of AI Agents and LangGraph Technical requirements Fundamentals of AI agents and RAG integration Graphs, AI agents, and LangGraph Code lab 12.1 – adding a LangGraph agent to RAG Core concepts of graph theory Nodes and edges in our agent Cyclical graph setup Summary
Chapter 13: Using Prompt Engineering to Improve RAG Efforts Chevron down icon Chevron up icon
Chapter 13: Using Prompt Engineering to Improve RAG Efforts Technical requirements Prompt parameters Take your shot Prompting, prompt design, and prompt engineering revisited Prompt design versus engineering approaches Fundamentals of prompt design Adapting prompts for different LLMs Code lab 13.1 – Custom prompt template Code lab 13.2 – Prompting options Summary
Chapter 14: Advanced RAG-Related Techniques for Improving Results Chevron down icon Chevron up icon
Chapter 14: Advanced RAG-Related Techniques for Improving Results Technical requirements Naïve RAG and its limitations Hybrid RAG/multi-vector RAG for improved retrieval Re-ranking in hybrid RAG Code lab 14.1 – Query expansion Code lab 14.2 – Query decomposition Code lab 14.3 – MM-RAG Other advanced RAG techniques to explore Summary
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Solutions Architect's Handbook
Saurabh Shrivastava and 1 more
Mar-24
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RAG-Driven Generative AI
Denis Rothman
Sep-24
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50 Algorithms Every Programmer Should Know
Imran Ahmad
Sep-23
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Building LLM Powered Applications
Valentina Alto
May-24
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Learn Python Programming
Fabrizio Romano and 3 more
Nov-24
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Learning Angular
Aristeidis Bampakos
Jan-25
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React Key Concepts
Maximilian Schwarzmüller
Nov-24
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Real-World Web Development with .NET 9
Mark J. Price
Dec-24
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Unlocking Data with Generative AI and RAG
Keith Bourne
Sep-24
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Expert reading lists

If you want to advance your tech knowledge but don't know where to start, explore Expert Reading Lists comprising our best titles on popular technologies grouped together by the Packt community.

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Modern Generative AI with ChatGPT and OpenAI Models Prompt Engineering For Everyone with ChatGPT and GPT-4 The Ultimate ChatGPT Guide with React Native - Develop Mobile Apps
Getting Started with ChatGPT
Stay ahead of the game and start unlocking the full potential of OpenAI and ChatGPT today.
Created: Oct-23
5 items in the reading list Icon
Modern Generative AI with ChatGPT and OpenAI Models Modern Generative AI with ChatGPT and OpenAI Models
Prompt Engineering For Everyone with ChatGPT and GPT-4 Prompt Engineering For Everyone with ChatGPT and GPT-4
The Ultimate ChatGPT Guide with React Native - Develop Mobile Apps The Ultimate ChatGPT Guide with React Native - Develop Mobile Apps
Make Apps with ChatGPT and Generative AI Make Apps with ChatGPT and Generative AI
Building a ChatGPT AI with JavaScript - An OpenAI Code-Along Guide Building a ChatGPT AI with JavaScript - An OpenAI Code-Along Guide
Mobile Artificial Intelligence Projects Hands-On Artificial Intelligence for Cybersecurity The Applied Artificial Intelligence Workshop
Artificial Intelligence Programming with Python
Swiftly grasp the art of building smart AI applications using Python. Learn AI basics and dive deeper into this captivating field. Develop your own smart apps with a focus on real-world cases and applying modern AI algorithms to diverse challenges.
Created: Oct-23
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Mobile Artificial Intelligence Projects Mobile Artificial Intelligence Projects
Hands-On Artificial Intelligence for Cybersecurity Hands-On Artificial Intelligence for Cybersecurity
The Applied Artificial Intelligence Workshop The Applied Artificial Intelligence Workshop
Practical Artificial Intelligence and Blockchain Practical Artificial Intelligence and Blockchain
Artificial Intelligence for Big Data Artificial Intelligence for Big Data
Artificial Intelligence with Python Artificial Intelligence with Python
Artificial Intelligence with Python Cookbook Artificial Intelligence with Python Cookbook
Hands-On Artificial Intelligence for Banking Hands-On Artificial Intelligence for Banking
Artificial Intelligence By Example Artificial Intelligence By Example
Hands-On Explainable AI (XAI) with Python Hands-On Explainable AI (XAI) with Python
Artificial Intelligence for IoT Cookbook Artificial Intelligence for IoT Cookbook
Transformers for Natural Language Processing Transformers for Natural Language Processing
Python for OOP - The A to Z OOP Python Programming Course Mastering Python 2E Functional Python Programming, 3rd edition
Building a Strong Foundation in Python
Explore this list for refining your Python skills and tackling business challenges. Discover techniques for writing clean code and building modern, high-performance, and scalable applications.
Created: Oct-23
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Python for OOP - The A to Z OOP Python Programming Course Python for OOP - The A to Z OOP Python Programming Course
Mastering Python 2E Mastering Python 2E
Functional Python Programming, 3rd edition Functional Python Programming, 3rd edition
PEP8 Guidelines: Python Clean Coding PEP8 Guidelines: Python Clean Coding
Complete Python Course with 10 Real-World Projects Complete Python Course with 10 Real-World Projects
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Top 10 New Releases

Stay up-to-date with all the latest additions to your library
Elevate Your Audio Production with REAPER Elevate Your Audio Production with REAPER
Mar-25
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Part 1: Basic Audio Production Icon Chapter 1: Getting Started with REAPER Icon Chapter 2: Recording Audio and MIDI in REAPER Icon Chapter 3: Editing and Arranging Icon Chapter 4: Mixing Procedures in REAPER Icon Chapter 5: Mastering and Exporting Projects Icon Part 2: Advanced Production Techniques Icon Chapter 6: Exploring Custom Actions and Macros Icon Chapter 7: Advanced Mixing and Processing Icon Chapter 8: Working with Video Icon Part 3: Specialized Applications Icon Chapter 9: Music Production Techniques Icon Chapter 10: Podcast Production Workflows Icon Chapter 11: Sound Design for Film and Games Icon Chapter 12: Exploring Immersive Audio Icon Chapter 13: Optimizing the Workflow and Troubleshooting in REAPER Icon Index Icon Other Books You May Enjoy Icon
AWS Certified Advanced Networking – Specialty (ANS-C01) Certification Guide AWS Certified Advanced Networking – Specialty (ANS-C01) Certification Guide
Nov-24
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Chapter 1: Advanced VPC Networking Icon Chapter 2: VPC Traffic and Performance Monitoring Icon Chapter 3: Networking Across Multiple AWS Accounts Icon Chapter 4: AWS Direct Connect Icon Chapter 5: Hybrid Networking with AWS Transit Gateway Icon Chapter 6: Connecting Third-Party Networks to AWS Icon Chapter 7: AWS Route 53: Basics Icon Chapter 8: AWS Route 53: Advanced Icon Chapter 9: AWS Elastic Load Balancing Icon Chapter 10: AWS CDN and Global Traffic Management Icon Chapter 11: Security Framework Icon Chapter 12: AWS Security Services Icon Chapter 13: Infrastructure as Code Icon Chapter 14: Data Analytics and Optimization Icon Chapter 15: Conclusion Icon Chapter 16: Accessing the Online Practice Resources Icon Other Books You May Enjoy Icon
The Infinite Retina The Infinite Retina
Jan-25
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Introduction Icon Part I: Why Spatial Computing and Why Now? Icon Prime Directive Icon Four Paradigms and Six Technologies Icon The Impact of Generative AI on Spatial Computing Icon Part II: The Seven Visions Icon Vision One: Transportation Automates Icon Vision Two: Virtual Worlds Appear Icon Vision Three: Augmented Manufacturing Icon Vision Four: Robot Consumers Icon Vision Five: Virtual Healthcare Icon Vision Six: Virtual Trading and Banking Icon Vision Seven: Real-Time Learning Icon Part III: The Spatial Business Icon The Always Predicted World Icon The Business of Generative AI Icon The Impact of the Apple Vision Pro Icon How Human? Icon Other Books You May Enjoy Icon Index Icon
Mastering AutoCAD 2025 Mastering AutoCAD 2025
Jan-25
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Part 1:The AutoCAD Interface – Enhanced Icon Chapter 1: Customizing the AutoCAD Interface Icon Chapter 2: Developing Your Own AutoCAD Workspace Icon Chapter 3: Using the AutoCAD Interface to Work Smarter Icon Chapter 4: Using Advanced Drafting Settings Icon Part 2:Advanced Drafting Techniques and Annotation Icon Chapter 5: Developing Advanced Geometry Using Object Snaps Icon Chapter 6: Measuring and Modifying Geometry and Objects Icon Chapter 7: Developing Annotative Annotation Styles Icon Chapter 8: Working with Annotative Scaling Icon Part 3:Content Re-Use Including Dynamic Blocks Icon Chapter 9: Developing Block Libraries Icon Chapter 10: Working with Block Attributes Icon Chapter 11: Creating a Dynamic Block with Parameters and Actions Icon Chapter 12: Creating a Dynamic Block with Visibility States Icon Part 4:Communicating and Collaborating Icon Chapter 13: Sharing Your AutoCAD Drawings Icon Chapter 14: Comparing Drawings and External Reference Files Icon Chapter 15: Working with AutoCAD Web Icon Chapter 16: Collaborating using Traces in AutoCAD Icon Index Icon Other Books You May Enjoy Icon
Unlocking Creativity with Azure OpenAI Unlocking Creativity with Azure OpenAI
Jan-25
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Part 1: Foundations of Generative AI and Azure OpenAI Icon Chapter 1: Introduction to Large Language Models Icon Chapter 2: Azure OpenAI Fundamentals Icon Chapter 3: Azure OpenAI Advanced Topics Icon Part 2: Practical Applications of Azure OpenAI: Real-World Use Cases Icon Chapter 4: Developing an Enterprise Document Question-Answer Solution Icon Chapter 5: Building a Contact Center Analytics Solution Icon Chapter 6: Querying From a Structured Database Icon Chapter 7: Code Generation and Documentation Icon Chapter 8: Creating a Basic Recommender Solution with Azure OpenAI Icon Chapter 9: Transforming Text to Video Icon Chapter 10: Creating a Multimodal Multi-Agent Framework with the Azure OpenAI Assistants API Icon Part 3: Mastering Governance, Operations, and AI Optimization with Azure OpenAI Icon Chapter 11: Privacy and Security Icon Chapter 12: Operationalizing Azure OpenAI Icon Chapter 13: Advanced Prompt Engineering Icon Index Icon Other Books You May Enjoy Icon
Learn Quantum Computing with Python and IBM Quantum Learn Quantum Computing with Python and IBM Quantum
Dec-24
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Exploring the IBM Quantum Tools Icon Creating Quantum Circuits with IBM Quantum Composer Icon Introducing and Installing Qiskit Icon Understanding Basic Quantum Computing Principles Icon Understanding the Qubit Icon Understanding Quantum Logic Gates Icon Programming with Qiskit Icon Optimizing and Visualizing Quantum Circuits Icon Simulating Quantum Systems and Noise Models Icon Suppressing and Mitigating Quantum Noise Icon Understanding Quantum Algorithms Icon Applying Quantum Algorithms Icon Understanding Quantum Utility and Qiskit Patterns Icon Other Book You May Enjoy Icon Index Icon

Top 10 Trending Titles

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LLM Engineer's Handbook LLM Engineer's Handbook
Oct-24
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Understanding the LLM Twin Concept and Architecture Icon Tooling and Installation Icon Data Engineering Icon RAG Feature Pipeline Icon Supervised Fine-Tuning Icon Fine-Tuning with Preference Alignment Icon Evaluating LLMs Icon Inference Optimization Icon RAG Inference Pipeline Icon Inference Pipeline Deployment Icon MLOps and LLMOps Icon Other Books You May Enjoy Icon Index Icon
C# 13 and .NET 9 – Modern Cross-Platform Development Fundamentals C# 13 and .NET 9 – Modern Cross-Platform Development Fundamentals
Nov-24
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Hello, C#! Welcome, .NET! Icon Speaking C# Icon Controlling Flow, Converting Types, and Handling Exceptions Icon Writing, Debugging, and Testing Functions Icon Building Your Own Types with Object-Oriented Programming Icon Implementing Interfaces and Inheriting Classes Icon Packaging and Distributing .NET Types Icon Working with Common .NET Types Icon Working with Files, Streams, and Serialization Icon Working with Data Using Entity Framework Core Icon Querying and Manipulating Data Using LINQ Icon Introducing Modern Web Development Using .NET Icon Building Websites Using ASP.NET Core Icon Building Interactive Web Components Using Blazor Icon Building and Consuming Web Services Icon Epilogue Icon Index Icon
Python Machine Learning By Example Python Machine Learning By Example
Jul-24
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Getting Started with Machine Learning and Python Icon Building a Movie Recommendation Engine with Naïve Bayes Icon Predicting Online Ad Click-Through with Tree-Based Algorithms Icon Predicting Online Ad Click-Through with Logistic Regression Icon Predicting Stock Prices with Regression Algorithms Icon Predicting Stock Prices with Artificial Neural Networks Icon Mining the 20 Newsgroups Dataset with Text Analysis Techniques Icon Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling Icon Recognizing Faces with Support Vector Machine Icon Machine Learning Best Practices Icon Categorizing Images of Clothing with Convolutional Neural Networks Icon Making Predictions with Sequences Using Recurrent Neural Networks Icon Advancing Language Understanding and Generation with the Transformer Models Icon Building an Image Search Engine Using CLIP: a Multimodal Approach Icon Making Decisions in Complex Environments with Reinforcement Learning Icon Other Books You May Enjoy Icon Index Icon
Python for Algorithmic Trading Cookbook Python for Algorithmic Trading Cookbook
Aug-24
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Chapter 1: Acquire Free Financial Market Data with Cutting-Edge Python Libraries Icon Chapter 2: Analyze and Transform Financial Market Data with pandas Icon Chapter 3: Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash Icon Chapter 4: Store Financial Market Data on Your Computer Icon Chapter 5: Build Alpha Factors for Stock Portfolios Icon Chapter 6: Vector-Based Backtesting with VectorBT Icon Chapter 7: Event-Based Backtesting Factor Portfolios with Zipline Reloaded Icon Chapter 8: Evaluate Factor Risk and Performance with Alphalens Reloaded Icon Chapter 9: Assess Backtest Risk and Performance Metrics with Pyfolio Icon Chapter 10: Set Up the Interactive Brokers Python API Icon Chapter 11: Manage Orders, Positions, and Portfolios with the IB API Icon Chapter 12: Deploy Strategies to a Live Environment Icon Chapter 13: Advanced Recipes for Market Data and Strategy Management Icon Index Icon Other Books You May Enjoy Icon
Machine Learning with PyTorch and Scikit-Learn Machine Learning with PyTorch and Scikit-Learn
Feb-22
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Giving Computers the Ability to Learn from Data Icon Training Simple Machine Learning Algorithms for Classification Icon A Tour of Machine Learning Classifiers Using Scikit-Learn Icon Building Good Training Datasets – Data Preprocessing Icon Compressing Data via Dimensionality Reduction Icon Learning Best Practices for Model Evaluation and Hyperparameter Tuning Icon Combining Different Models for Ensemble Learning Icon Applying Machine Learning to Sentiment Analysis Icon Predicting Continuous Target Variables with Regression Analysis Icon Working with Unlabeled Data – Clustering Analysis Icon Implementing a Multilayer Artificial Neural Network from Scratch Icon Parallelizing Neural Network Training with PyTorch Icon Going Deeper – The Mechanics of PyTorch Icon Classifying Images with Deep Convolutional Neural Networks Icon Modeling Sequential Data Using Recurrent Neural Networks Icon Transformers – Improving Natural Language Processing with Attention Mechanisms Icon Generative Adversarial Networks for Synthesizing New Data Icon Graph Neural Networks for Capturing Dependencies in Graph Structured Data Icon Reinforcement Learning for Decision Making in Complex Environments Icon Other Books You May Enjoy Icon Index Icon
RAG-Driven Generative AI RAG-Driven Generative AI
Sep-24
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Why Retrieval Augmented Generation? Icon RAG Embedding Vector Stores with Deep Lake and OpenAI Icon Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI Icon Multimodal Modular RAG for Drone Technology Icon Boosting RAG Performance with Expert Human Feedback Icon Scaling RAG Bank Customer Data with Pinecone Icon Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex Icon Dynamic RAG with Chroma and Hugging Face Llama Icon Empowering AI Models: Fine-Tuning RAG Data and Human Feedback Icon RAG for Video Stock Production with Pinecone and OpenAI Icon Other Books You May Enjoy Icon Index Icon
Solutions Architect's Handbook Solutions Architect's Handbook
Mar-24
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Solutions Architects in Organizations Icon Principles of Solution Architecture Design Icon Cloud Migration and Cloud Architecture Design Icon Solution Architecture Design Patterns Icon Cloud-Native Architecture Design Patterns Icon Performance Considerations Icon Security Considerations Icon Architectural Reliability Considerations Icon Operational Excellence Considerations Icon Cost Considerations Icon DevOps and Solution Architecture Framework Icon Data Engineering for Solution Architecture Icon Machine Learning Architecture Icon Generative AI Architecture Icon Rearchitecting Legacy Systems Icon Solution Architecture Document Icon Learning Soft Skills to Become a Better Solutions Architect Icon Other Books You May Enjoy Icon Index Icon
50 Algorithms Every Programmer Should Know 50 Algorithms Every Programmer Should Know
Sep-23
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Section 1: Fundamentals and Core Algorithms Icon Overview of Algorithms Icon Data Structures Used in Algorithms Icon Sorting and Searching Algorithms Icon Designing Algorithms Icon Graph Algorithms Icon Section 2: Machine Learning Algorithms Icon Unsupervised Machine Learning Algorithms Icon Traditional Supervised Learning Algorithms Icon Neural Network Algorithms Icon Algorithms for Natural Language Processing Icon Understanding Sequential Models Icon Advanced Sequential Modeling Algorithms Icon Section 3: Advanced Topics Icon Recommendation Engines Icon Algorithmic Strategies for Data Handling Icon Cryptography Icon Large-Scale Algorithms Icon Practical Considerations Icon Other Books You May Enjoy Icon Index Icon
Building LLM Powered Applications Building LLM Powered Applications
May-24
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Introduction to Large Language Models Icon LLMs for AI-Powered Applications Icon Choosing an LLM for Your Application Icon Prompt Engineering Icon Embedding LLMs within Your Applications Icon Building Conversational Applications Icon Search and Recommendation Engines with LLMs Icon Using LLMs with Structured Data Icon Working with Code Icon Building Multimodal Applications with LLMs Icon Fine-Tuning Large Language Models Icon Responsible AI Icon Emerging Trends and Innovations Icon Other Books You May Enjoy Icon Index Icon
Learn Python Programming Learn Python Programming
Nov-24
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A Gentle Introduction to Python Icon Built-In Data Types Icon Conditionals and Iteration Icon Functions, the Building Blocks of Code Icon Comprehensions and Generators Icon OOP, Decorators, and Iterators Icon Exceptions and Context Managers Icon Files and Data Persistence Icon Cryptography and Tokens Icon Testing Icon Debugging and Profiling Icon Introduction to Type Hinting Icon Data Science in Brief Icon Introduction to API Development Icon CLI Applications Icon Packaging Python Applications Icon Programming Challenges Icon Other Books You May Enjoy Icon Index Icon

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