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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
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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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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Practical Generative AI with ChatGPT
Practical Generative AI with ChatGPT
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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
Other Books You May Enjoy
Index Chevron down icon Chevron up icon
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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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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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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The Ultimate Linux Shell Scripting Guide
The Ultimate Linux Shell Scripting Guide
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Full star icon Full star icon Full star icon Full star icon Full star icon 5
By Donald A. Tevault
October 2024 | 696 pages
Icon Embrace Bash, Zsh, and PowerShell across Linux, macOS, Unix
Icon Seamlessly progress through chapters with conceptual explanations, illustrative examples, and immersive labs for hands-on skill development
Icon Build real-world Linux administration scripts, enhancing your troubleshooting and management skills
Getting Started with the Shell Chevron down icon Chevron up icon
Getting Started with the Shell Understanding Shells Finding Help with Shell Commands Using a Text Editor to Create Shell Scripts Understanding Compiled versus Interpreted Programming Understanding root and sudo Privileges Summary Questions Further Reading Answers Join our community on Discord!
Interpreting Commands Chevron down icon Chevron up icon
Interpreting Commands Understanding the Structure of a Command Executing Multiple Commands at Once Running Commands Recursively Understanding the Command History Escaping and Quoting Summary Questions Further Reading Answers Join our community on Discord!
Understanding Variables and Pipelines Chevron down icon Chevron up icon
Understanding Variables and Pipelines Understanding Environmental Variables Understanding Programming Variables Understanding Pipelines Summary Questions Further Reading Answers Join our community on Discord!
Understanding Input/Output Redirection Chevron down icon Chevron up icon
Understanding Input/Output Redirection Introduction to Input/Output Redirection Understanding stdout Understanding stdin Understanding stderr Understanding tee Hands-on Lab – Pipes, Redirectors, and find Summary Questions Further Reading Answers Join our community on Discord!
Customizing the Environment Chevron down icon Chevron up icon
Customizing the Environment Technical Requirements Reviewing the Environmental Variables Understanding Shell Sessions Understanding the Configuration Files Setting Shell Options from the Command-line Understanding Aliases Summary Questions Further Reading Answers Join our community on Discord!
Text-Stream Filters – Part 1 Chevron down icon Chevron up icon
Text-Stream Filters – Part 1 Technical Requirements Introduction to Text-Stream Filters Using cat Using tac Using cut Using paste Using join Using sort Summary Questions Further Reading Answers Join our community on Discord!
Text Stream Filters – Part 2 Chevron down icon Chevron up icon
Text Stream Filters – Part 2 Technical Requirements Using expand Using unexpand Using nl Using head Using tail Using Head And Tail Together Using od Using uniq Using wc Using fmt Using split Using tr Using xargs Using pr Printing from the Command-line Summary Questions Further Reading Answers Join our community on Discord!
Basic Shell Script Construction Chevron down icon Chevron up icon
Basic Shell Script Construction Technical Requirements Understanding Basic Shell Script Construction Performing Tests Understanding Subshells Hands-on Lab – Testing Conditions Understanding Scripting Variables Understanding Array Variables Understanding Variable Expansion Understanding Command Substitution Understanding Decisions and Loops Understanding Exit Codes More Information About echo Looking at Some Real-World Examples Summary Questions Further Reading Answers Join our community on Discord!
Filtering Text with grep, sed, and Regular Expressions Chevron down icon Chevron up icon
Filtering Text with grep, sed, and Regular Expressions Technical Requirements Understanding Regular Expressions Understanding sed Understanding grep Using RegEx Helper Programs Looking at Some Real-World Examples Summary Questions Further Reading Answers Join our community on Discord!
Understanding Functions Chevron down icon Chevron up icon
Understanding Functions Technical Requirements Introduction to Functions Defining a Function Using Functions in Shell Scripts Creating Function Libraries Looking at Some Real-World Examples Summary Questions Further Reading Answers Join our community on Discord!
Performing Mathematical Operations Chevron down icon Chevron up icon
Performing Mathematical Operations Technical Requirements Performing Integer Math with Expressions Performing Integer Math with Integer Variables Performing Floating Point Math with bc Summary Questions Further Reading Answers Join our community on Discord!
Automating Scripts with here Documents and expect Chevron down icon Chevron up icon
Automating Scripts with here Documents and expect Technical Requirements Using here Documents Automating Responses with expect Summary Questions Further Reading Answers Join our community on Discord!
Scripting with ImageMagick Chevron down icon Chevron up icon
Scripting with ImageMagick Technical Requirements Converting Non-standard Filename Extensions Installing ImageMagick Resizing and Customizing Images Batch-processing Image Files Using Fred’s ImageMagick Scripts Summary Questions Further Reading Answers Join our community on Discord!
Using awk – Part 1 Chevron down icon Chevron up icon
Using awk – Part 1 Introducing awk Understanding Patterns and Actions Obtaining Input from Text Files Obtaining Input from Commands Summary Questions Further Reading Answers Join our community on Discord!
Using awk – Part 2 Chevron down icon Chevron up icon
Using awk – Part 2 Technical Requirements Basic awk Script Construction Using Conditional Statements Using a while Construct and Setting Variables Using for loops and Arrays Using Floating Point Math and printf Working with Multi-Line Records Summary Questions Further Reading Answers Join our community on Discord!
Creating User Interfaces with yad, dialog, and xdialog Chevron down icon Chevron up icon
Creating User Interfaces with yad, dialog, and xdialog Technical Requirements Creating a Graphical User Interface with yad Creating User Interfaces with dialog and xdialog Summary Questions Further Reading Answers Join our community on Discord!
Using Shell Script Options with getops Chevron down icon Chevron up icon
Using Shell Script Options with getops Technical Requirements Understanding the Need for getopts Understanding getopt versus getopts Using getopts Looking at Real-world Examples Summary Questions Further Reading Answers Join our community on Discord!
Shell Scripting for Security Professionals Chevron down icon Chevron up icon
Shell Scripting for Security Professionals Technical Requirements Simple Scripts for Auditing Creating Simple Firewall Scripts Searching for Existing Security-related Scripts Summary Questions Further Reading Answers Join our community on Discord!
Shell Script Portability Chevron down icon Chevron up icon
Shell Script Portability Technical Requirements Running bash on Non-Linux Systems Understanding POSIX compliance Understanding the Differences Between Shells Understanding Bashisms Testing Scripts for POSIX Compliance Summary Questions Further Reading Answers Join our community on Discord!
Shell Script Security Chevron down icon Chevron up icon
Shell Script Security Technical Requirements Controlling Access to Your Scripts Understanding SUID and SGID Considerations Avoiding Sensitive Data Leakage Understanding Command Injection with eval Understanding Path Security Summary Questions Further Reading Answers Join our community on Discord!
Debugging Shell Scripts Chevron down icon Chevron up icon
Debugging Shell Scripts Technical Requirements Understanding Common Scripting Errors Using Shell Script Debugging Tools and Techniques Summary Questions Further Reading Answers Join our community on Discord!
Introduction to Z Shell Scripting Chevron down icon Chevron up icon
Introduction to Z Shell Scripting Technical Requirements Introducing zsh Installing zsh Understanding the Unique Features of zsh Scripting Using zsh Modules Summary Questions Further Reading Answers Join our community on Discord!
Using PowerShell on Linux Chevron down icon Chevron up icon
Using PowerShell on Linux Technical Requirements Installing PowerShell on Linux and macOS Reasons for Linux and Mac Admins to Learn PowerShell Differences Between PowerShell Scripting and Traditional Linux/Unix Scripting Viewing the Available PowerShell Commands Getting Help with PowerShell Commands Real-World Cross-Platform PowerShell Scripts Summary Further Reading
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Solutions Architect's Handbook
Solutions Architect's Handbook
Read now
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
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Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn
Read now
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
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Kubernetes – An Enterprise Guide
Kubernetes – An Enterprise Guide
Read now
Full star icon Full star icon Full star icon Full star icon Full star icon 5
By Marc Boorshtein
August 2024 | 682 pages
Icon Focuses on what you need to run Kubernetes in an Enterprise environment, based on real-world experience
Icon Learn how to secure clusters by adding runtime security and enhancing secrets management using direct pod mounting and Vault integration
Icon Get a firm command of Kubernetes from a dual perspective of an admin as well as a developer
Docker and Container Essentials Chevron down icon Chevron up icon
Docker and Container Essentials Technical requirements Understanding the need for containerization Understanding why Kubernetes removed Docker Understanding Docker Installing Docker Using the Docker CLI Summary Questions Join our book’s Discord space
Deploying Kubernetes Using KinD Chevron down icon Chevron up icon
Deploying Kubernetes Using KinD Technical requirements Introducing Kubernetes components and objects Using development clusters Installing KinD Creating a KinD cluster Reviewing your KinD cluster Adding a custom load balancer for Ingress Summary Questions
Kubernetes Bootcamp Chevron down icon Chevron up icon
Kubernetes Bootcamp Technical requirements An overview of Kubernetes components Exploring the control plane Understanding the worker node components Interacting with the API server Introducing Kubernetes resources Summary Questions Join our book’s Discord space
Services, Load Balancing, and Network Policies Chevron down icon Chevron up icon
Services, Load Balancing, and Network Policies Technical requirements Exposing workloads to requests Introduction to load balancers Layer 7 load balancers Layer 4 load balancers Introducing Network Policies Summary Questions
External DNS and Global Load Balancing Chevron down icon Chevron up icon
External DNS and Global Load Balancing Technical requirements Making service names available externally Exposing CoreDNS to external requests Load balancing between multiple clusters Summary Questions Join our book’s Discord space
Integrating Authentication into Your Cluster Chevron down icon Chevron up icon
Integrating Authentication into Your Cluster Technical requirements Getting Help Understanding how Kubernetes knows who you are Understanding OpenID Connect Configuring KinD for OpenID Connect Introducing impersonation to integrate authentication with cloud-managed clusters Configuring your cluster for impersonation Authenticating from pipelines Summary Questions Answers
RBAC Policies and Auditing Chevron down icon Chevron up icon
RBAC Policies and Auditing Technical requirements Introduction to RBAC Mapping enterprise identities to Kubernetes to authorize access to resources Implementing namespace multi-tenancy Kubernetes auditing Using audit2rbac to debug policies Summary Questions Answers Join our book’s Discord space
Managing Secrets Chevron down icon Chevron up icon
Managing Secrets Technical Requirements Getting Help Examining the difference between Secrets and Configuration Data Understanding Secrets Managers Integrating Secrets into Your Deployments Summary Questions Answers
Building Multitenant Clusters with vClusters Chevron down icon Chevron up icon
Building Multitenant Clusters with vClusters Technical requirements Getting Help The Benefits and Challenges of Multitenancy Using vClusters for Tenants Building a Multitenant Cluster with Self Service Summary Questions Answers Join our book’s Discord space
Deploying a Secured Kubernetes Dashboard Chevron down icon Chevron up icon
Deploying a Secured Kubernetes Dashboard Technical requirements How does the dashboard know who you are? Understanding dashboard security risks Deploying the dashboard with a reverse proxy Integrating the dashboard with OpenUnison What’s changed in the Kubernetes Dashboard 7.0 Summary Questions Answers
Extending Security Using Open Policy Agent Chevron down icon Chevron up icon
Extending Security Using Open Policy Agent Technical requirements Introduction to dynamic admission controllers What is OPA and how does it work? Using Rego to write policies Enforcing Ingress policies Mutating objects and default values Creating policies without Rego Summary Questions Answers Join our book’s Discord space
Node Security with Gatekeeper Chevron down icon Chevron up icon
Node Security with Gatekeeper Technical requirements What is node security? Enforcing node security with Gatekeeper Using Pod Security Standards to enforce Node Security Summary Questions Answers
KubeArmor Securing Your Runtime Chevron down icon Chevron up icon
KubeArmor Securing Your Runtime Technical requirements What is runtime security? Introducing KubeArmor Deploying KubeArmor Enabling KubeArmor logging KubeArmor and LSM policies Creating a KubeArmorSecurityPolicy Using karmor to interact with KubeArmor Summary Questions Answers Join our book’s Discord space
Backing Up Workloads Chevron down icon Chevron up icon
Backing Up Workloads Technical requirements Understanding Kubernetes backups Performing an etcd backup Introducing and setting up VMware’s Velero Using Velero to back up workloads and PVCs Managing Velero using the CLI Restoring from a backup Summary Questions Answers
Monitoring Clusters and Workloads Chevron down icon Chevron up icon
Monitoring Clusters and Workloads Technical Requirements Managing Metrics in Kubernetes How Kubernetes Provides Metrics Deploying the Prometheus Stack Log Management in Kubernetes Summary Questions Answers Join our book’s Discord space
An Introduction to Istio Chevron down icon Chevron up icon
An Introduction to Istio Technical requirements Understanding the Control Plane and Data Plane Why should you care about a Service mesh? Introduction to Istio concepts Installing Istio Introducing Istio resources Deploying add-on components to provide observability Deploying an application into the Service mesh The future: Ambient mesh Summary Questions Answers
Building and Deploying Applications on Istio Chevron down icon Chevron up icon
Building and Deploying Applications on Istio Technical requirements Comparing microservices and monoliths Deploying a monolith Building a microservice Do I need an API gateway? Summary Questions Join our book’s Discord space
Provisioning a Multitenant Platform Chevron down icon Chevron up icon
Provisioning a Multitenant Platform Technical requirements Designing a pipeline Designing our platform architecture Using Infrastructure as Code for deployment Automating tenant onboarding Considerations for building an Internal Developer Platform Summary Questions Answers
Building a Developer Portal Chevron down icon Chevron up icon
Building a Developer Portal Technical Requirements Deploying our IDP Onboarding a Tenant Deploying an Application Adding Users to a Tenant Expanding Our Platform Summary Questions Answers Join our book’s Discord space
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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
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Part 1:Getting Started with Studio One Icon Chapter 1: Choosing Your Flavor of Studio One Icon Chapter 2: Installing and Configuring Your Studio One Environment Icon Chapter 3: Exploring the Studio One Environment Icon Part 2: Creating in Studio One Icon Chapter 4: Creating a New Song Icon Chapter 5: Recording Your First Audio Track Icon Chapter 6: Adding Virtual Instruments and Recording MIDI Icon Part 3: Editing in Studio One Icon Chapter 7: Editing and Rearranging Your Song Icon Chapter 8: Editing Audio Icon Chapter 9: Editing MIDI Icon Chapter 10: Using Global Tracks Icon Part 4: Mixing and Mastering Icon Chapter 11: Preparing for the Mix Icon Chapter 12: Working with Effects Plug-Ins to Craft a Mix Icon Chapter 13: Optimizing Signal Flow and Elevating Your Mix Icon Chapter 14: Working with Spatial Audio Icon Chapter 15: Navigating the Project Page and Producing Final Masters Icon Chapter 16: Using Additional Studio One Features Icon Index Icon Other Books You May Enjoy Icon

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