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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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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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Index
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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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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
Other Books You May Enjoy
Index Chevron down icon Chevron up icon
Index
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Generative AI with Python and PyTorch
Generative AI with Python and PyTorch
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By Joseph Babcock
December 2024 | 500 pages
Icon Implement real-world applications of LLMs and generative AI
Icon Use PEFT and LoRA to fine-tune models with a subset of the model weights to speed up training
Icon Enhance your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndex
Icon Purchase of the print or Kindle book includes a free eBook in PDF format
Generative AI with Python and PyTorch, Second Edition: Hands-on projects and cutting-edge techniques using generative adversarial networks and LLMs Chevron down icon Chevron up icon
Generative AI with Python and PyTorch, Second Edition: Hands-on projects and cutting-edge techniques using generative adversarial networks and LLMs
1 Introduction to Generative AI: “Drawing” Data from Models Chevron down icon Chevron up icon
Join our book community on Discord Introduction The rules of probability Discriminative and generative modeling, and Bayes’ theorem Why generative models? Unique challenges of generative models Summary References
2 Building Blocks of Deep Neural Networks Chevron down icon Chevron up icon
Join our book community on Discord Introduction Perceptrons—a brain in a function Multi-layer perceptrons and backpropagation Varieties of networks: Convolution and recursive Networks for sequential data Transformers Building a better optimizer Summary References
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Practical Generative AI with ChatGPT
Practical Generative AI with ChatGPT
Read now
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
By Valentina Alto
February 2025 | 0 pages
Icon Turn ChatGPT into your ultimate assistant and free up your time
Icon Supercharge your creativity and productivity for research, marketing, data analysis, and coding
Icon Get started with generative AI and learn the basics of how models like ChatGPT work
Fundamentals of Generative AI and OpenAI Chevron down icon Chevron up icon
Fundamentals of Generative AI and OpenAI
Introduction to Generative AI Chevron down icon Chevron up icon
Introduction to Generative AI Introducing generative AI Domains of generative AI Main trends and innovations Legal and ethical landscape of generative AI Summary References
OpenAI and ChatGPT: Beyond the Market Hype Chevron down icon Chevron up icon
OpenAI and ChatGPT: Beyond the Market Hype Technical requirements What is OpenAI? An overview of OpenAI model families Getting started with ChatGPT Summary References
ChatGPT in Action Chevron down icon Chevron up icon
ChatGPT in Action
Understanding Prompt Engineering Chevron down icon Chevron up icon
Understanding Prompt Engineering Technical requirements What is prompt engineering? Understanding zero-, one-, and few-shot learning Principles of prompt engineering Exploring some advanced techniques Ethical considerations to avoid bias Summary References
Boosting Day-to-Day Productivity with ChatGPT Chevron down icon Chevron up icon
Boosting Day-to-Day Productivity with ChatGPT Technical requirements ChatGPT as a daily assistant Generating text Improving writing skills and translation Quick information retrieval and competitive intelligence Summary
Developing the Future with ChatGPT Chevron down icon Chevron up icon
Developing the Future with ChatGPT Technical requirements Why should developers use ChatGPT? Generating, optimizing, and debugging code Generating documentation and code explainability Understanding ML model interpretability Translation among different programming languages Working with code on canvas Summary
Mastering Marketing with ChatGPT Chevron down icon Chevron up icon
Mastering Marketing with ChatGPT Technical requirements Leveraging ChatGPT for marketing New product development and the go-to-market strategy A/B testing for marketing comparison Boosting SEO Sentiment analysis for quality and customer satisfaction Summary
Research Reinvented with ChatGPT Chevron down icon Chevron up icon
Research Reinvented with ChatGPT Researchers’ need for ChatGPT Brainstorming literature for your study Providing support for the design and framework of your experiment Generating and formatting a bibliography Generating a presentation of the study Summary References
Unleashing Creativity Visually with ChatGPT Chevron down icon Chevron up icon
Unleashing Creativity Visually with ChatGPT What is multimodality? Prompt design to generate stunning illustrations with DALL-E Leveraging ChatGPT as a designer assistant Exploring advanced plugins within the GPT store Summary References
Exploring GPTs Chevron down icon Chevron up icon
Exploring GPTs Technical requirements What are GPTs? Personal assistant Code assistant Marketing assistant Research assistant Summary References
OpenAI for Enterprises Chevron down icon Chevron up icon
OpenAI for Enterprises
Leveraging OpenAI’s Models for Enterprise-Scale Applications Chevron down icon Chevron up icon
Leveraging OpenAI’s Models for Enterprise-Scale Applications Technical requirements How GenAI is disrupting industries Understanding OpenAI models’ APIs Architectural patterns to build applications with models’ APIs Introducing the public cloud: Azure OpenAI Summary References
Epilogue and Final Thoughts Chevron down icon Chevron up icon
Epilogue and Final Thoughts An overview of what we have learned so far It’s not all about OpenAI Ethical implications of generative AI and why we need responsible AI What to expect in the near future Summary References
Other Books You May Enjoy Chevron down icon Chevron up icon
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Index Chevron down icon Chevron up icon
Index
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LLM Engineer's Handbook LLM Engineer's Handbook
Oct-24
Full star icon Full star icon Full star icon Full star icon Half star icon
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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
Generative AI with Python and PyTorch Generative AI with Python and PyTorch
Dec-24
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Generative AI with Python and PyTorch, Second Edition: Hands-on projects and cutting-edge techniques using generative adversarial networks and LLMs Icon 1 Introduction to Generative AI: “Drawing” Data from Models Icon 2 Building Blocks of Deep Neural Networks 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
Practical Generative AI with ChatGPT Practical Generative AI with ChatGPT
Feb-25
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Fundamentals of Generative AI and OpenAI Icon Introduction to Generative AI Icon OpenAI and ChatGPT: Beyond the Market Hype Icon ChatGPT in Action Icon Understanding Prompt Engineering Icon Boosting Day-to-Day Productivity with ChatGPT Icon Developing the Future with ChatGPT Icon Mastering Marketing with ChatGPT Icon Research Reinvented with ChatGPT Icon Unleashing Creativity Visually with ChatGPT Icon Exploring GPTs Icon OpenAI for Enterprises Icon Leveraging OpenAI’s Models for Enterprise-Scale Applications Icon Epilogue and Final Thoughts Icon Other Books You May Enjoy Icon Index Icon
Python Machine Learning By Example Python Machine Learning By Example
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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

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