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Mathematics of Machine Learning
Mathematics of Machine Learning
By Tivadar Danka
May 2025 | 730 pages
Icon Master linear algebra, calculus, and probability theory for ML
Icon Bridge the gap between theory and real-world applications
Icon Learn Python implementations of core mathematical concepts
Introduction Chevron down icon Chevron up icon
Part 1: Linear Algebra Chevron down icon Chevron up icon
1 Vectors and Vector Spaces Chevron down icon Chevron up icon
2 The Geometric Structure of Vector Spaces Chevron down icon Chevron up icon
3 Linear Algebra in Practice Chevron down icon Chevron up icon
4 Linear Transformations Chevron down icon Chevron up icon
5 Matrices and Equations Chevron down icon Chevron up icon
6 Eigenvalues and Eigenvectors Chevron down icon Chevron up icon
7 Matrix Factorizations Chevron down icon Chevron up icon
8 Matrices and Graphs Chevron down icon Chevron up icon
References Chevron down icon Chevron up icon
Part 2: Calculus Chevron down icon Chevron up icon
9 Functions Chevron down icon Chevron up icon
10 Numbers, Sequences, and Series Chevron down icon Chevron up icon
11 Topology, Limits, and Continuity Chevron down icon Chevron up icon
12 Differentiation Chevron down icon Chevron up icon
13 Optimization Chevron down icon Chevron up icon
14 Integration Chevron down icon Chevron up icon
References Chevron down icon Chevron up icon
Part 3: Multivariable Calculus Chevron down icon Chevron up icon
15 Multivariable Functions Chevron down icon Chevron up icon
16 Derivatives and Gradients Chevron down icon Chevron up icon
17 Optimization in Multiple Variables Chevron down icon Chevron up icon
References Chevron down icon Chevron up icon
Part 4: Probability Theory Chevron down icon Chevron up icon
18 What is Probability? Chevron down icon Chevron up icon
19 Random Variables and Distributions Chevron down icon Chevron up icon
20 The Expected Value Chevron down icon Chevron up icon
References Chevron down icon Chevron up icon
Part 5: Appendix Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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Building Agentic AI Systems
Building Agentic AI Systems
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
By Anjanava Biswas
April 2025 | 288 pages
Icon Understand the foundations and advanced techniques of building intelligent, autonomous AI agents
Icon Learn advanced techniques for reflection, introspection, tool use, planning, and collaboration in agentic systems
Icon Explore crucial aspects of trust, safety, and ethics in AI agent development and applications
Icon Purchase of the print or Kindle book includes a free PDF eBook
Part 1: Foundations of Generative AI and Agentic Systems Chevron down icon Chevron up icon
Chapter 1: Fundamentals of Generative AI Chevron down icon Chevron up icon
Chapter 2: Principles of Agentic Systems Chevron down icon Chevron up icon
Chapter 3: Essential Components of Intelligent Agents Chevron down icon Chevron up icon
Part 2: Designing and Implementing Generative AI-Based Agents Chevron down icon Chevron up icon
Chapter 4: Reflection and Introspection in Agents Chevron down icon Chevron up icon
Chapter 5: Enabling Tool Use and Planning in Agents Chevron down icon Chevron up icon
Chapter 6: Exploring the Coordinator, Worker, and Delegator Approach Chevron down icon Chevron up icon
Chapter 7: Effective Agentic System Design Techniques Chevron down icon Chevron up icon
Part 3: Trust, Safety, Ethics, and Applications Chevron down icon Chevron up icon
Chapter 8: Building Trust in Generative AI Systems Chevron down icon Chevron up icon
Chapter 9: Managing Safety and Ethical Considerations Chevron down icon Chevron up icon
Chapter 10: Common Use Cases and Applications Chevron down icon Chevron up icon
Chapter 11: Conclusion and Future Outlook Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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LLM Engineer's Handbook
LLM Engineer's Handbook
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
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
Tooling and Installation Chevron down icon Chevron up icon
Data Engineering Chevron down icon Chevron up icon
RAG Feature Pipeline Chevron down icon Chevron up icon
Supervised Fine-Tuning Chevron down icon Chevron up icon
Fine-Tuning with Preference Alignment Chevron down icon Chevron up icon
Evaluating LLMs Chevron down icon Chevron up icon
Inference Optimization Chevron down icon Chevron up icon
RAG Inference Pipeline Chevron down icon Chevron up icon
Inference Pipeline Deployment Chevron down icon Chevron up icon
MLOps and LLMOps Chevron down icon Chevron up icon
MLOps Principles Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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Building AI Agents with LLMs, RAG, and Knowledge Graphs
Building AI Agents with LLMs, RAG, and Knowledge Graphs
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
By Salvatore Raieli
July 2025 | 560 pages
Icon Implement RAG and knowledge graphs for advanced problem-solving
Icon Leverage innovative approaches like LangChain to create real-world intelligent systems
Icon Integrate large language models, graph databases, and tool use for next-gen AI solutions
Icon Purchase of the print or Kindle book includes a free PDF eBook
Part 1: The AI Agent Engine: From Text to Large Language Models Chevron down icon Chevron up icon
Chapter 1: Analyzing Text Data with Deep Learning Chevron down icon Chevron up icon
Chapter 2: The Transformer: The Model Behind the Modern AI Revolution Chevron down icon Chevron up icon
Chapter 3: Exploring LLMs as a Powerful AI Engine Chevron down icon Chevron up icon
Part 2: AI Agents and Retrieval of Knowledge Chevron down icon Chevron up icon
Chapter 4: Building a Web Scraping Agent with an LLM Chevron down icon Chevron up icon
Chapter 5: Extending Your Agent with RAG to Prevent Hallucinations Chevron down icon Chevron up icon
Chapter 6: Advanced RAG Techniques for Information Retrieval and Augmentation Chevron down icon Chevron up icon
Chapter 7: Creating and Connecting a Knowledge Graph to an AI Agent Chevron down icon Chevron up icon
Chapter 8: Reinforcement Learning and AI Agents Chevron down icon Chevron up icon
Part 3: Creating Sophisticated AI to Solve Complex Scenarios Chevron down icon Chevron up icon
Chapter 9: Creating Single- and Multi-Agent Systems Chevron down icon Chevron up icon
Chapter 10: Building an AI Agent Application Chevron down icon Chevron up icon
Chapter 11: The Future Ahead Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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Machine Learning Product Management - Strategy to Deployment
Machine Learning Product Management - Strategy to Deployment
By Raj Elakkara
August 2025 | 285 pages
Icon Comprehensive coverage of machine learning concepts and product management strategies
Icon Practical exercises to reinforce learning and real-world application of ML techniques
Icon In-depth guidance on model evaluation, optimization, and deployment across industries
Getting Started with Machine Learning Chevron down icon Chevron up icon
Decision Criteria for Machine Learning Implementation Chevron down icon Chevron up icon
Managing Machine Learning Projects Chevron down icon Chevron up icon
Data Acquisition and Preparation for Machine Learning Chevron down icon Chevron up icon
Preprocessing Techniques for Machine Learning Chevron down icon Chevron up icon
Algorithm Selection and ML Solution Development Chevron down icon Chevron up icon
Model Evaluation Metrics and Performance Optimization Chevron down icon Chevron up icon
ML Model Deployment and Monitoring Chevron down icon Chevron up icon
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Generative AI with LangChain
Generative AI with LangChain
By Ben Auffarth
May 2025 | 480 pages
Icon Bridge the gap between prototype and production with robust LangGraph agent architectures
Icon Apply enterprise-grade practices for testing, observability, and monitoring
Icon Build specialized agents for software development and data analysis
Icon Purchase of the print or Kindle book includes a free PDF eBook
The Rise of Generative AI: From Language Models to Agents Chevron down icon Chevron up icon
First Steps with LangChain Chevron down icon Chevron up icon
Building Workflows with LangGraph Chevron down icon Chevron up icon
Building Intelligent RAG Systems Chevron down icon Chevron up icon
Building Intelligent Agents Chevron down icon Chevron up icon
Advanced Applications and Multi-Agent Systems Chevron down icon Chevron up icon
Software Development and Data Analysis Agents Chevron down icon Chevron up icon
Evaluation and Testing Chevron down icon Chevron up icon
Production-Ready LLM Deployment and Observability Chevron down icon Chevron up icon
The Future of Generative Models: Beyond Scaling Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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AI Engineer Explorer Course
AI Engineer Explorer Course
By Vivian Aranha
July 2025 | 761 pages
Icon Comprehensive coverage of Python programming tailored for AI applications and development
Icon Deep dive into essential data science tools, preparing you to manipulate and visualize data efficiently
Icon In-depth exploration of core AI mathematics, enabling better model development and understanding
Introduction to Course and Instructor Chevron down icon Chevron up icon
Python Programming Basics for Artificial Intelligence Chevron down icon Chevron up icon
Data Science Essentials for Artificial Intelligence Chevron down icon Chevron up icon
Mathematics for Machine Learning and Artificial Intelligence Chevron down icon Chevron up icon
Probability and Statistics for Machine Learning and Artificial Intelligence Chevron down icon Chevron up icon
Introduction to Machine Learning Chevron down icon Chevron up icon
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Graph Machine Learning
Graph Machine Learning
By Aldo Marzullo
July 2025 | 434 pages
Icon Master new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL)
Icon Explore GML frameworks and their main characteristics
Icon Leverage LLMs for machine learning on graphs and learn about temporal learning
Icon Purchase of the print or Kindle book includes a free PDF eBook
Part 1: Introduction to Graph Machine Learning Chevron down icon Chevron up icon
Getting Started with Graphs Chevron down icon Chevron up icon
Graph Machine Learning Chevron down icon Chevron up icon
Neural Networks and Graphs Chevron down icon Chevron up icon
Part 2: Machine Learning on Graphs Chevron down icon Chevron up icon
Unsupervised Graph Learning Chevron down icon Chevron up icon
Supervised Graph Learning Chevron down icon Chevron up icon
Solving Common Graph-Based Machine Learning Problems Chevron down icon Chevron up icon
Part 3: Practical Applications of Graph Machine Learning Chevron down icon Chevron up icon
Social Network Graphs Chevron down icon Chevron up icon
Text Analytics and Natural Language Processing Using Graphs Chevron down icon Chevron up icon
Graph Analysis for Credit Card Transactions Chevron down icon Chevron up icon
Building a Data-Driven Graph-Powered Application Chevron down icon Chevron up icon
Part 4: Advanced topics in Graph Machine Learning Chevron down icon Chevron up icon
Temporal Graph Machine Learning Chevron down icon Chevron up icon
GraphML and LLMs Chevron down icon Chevron up icon
Novel Trends on Graphs Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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Building Business-Ready Generative AI Systems
Building Business-Ready Generative AI Systems
By Denis Rothman
July 2025 | 444 pages
Icon Build an adaptive, context-aware AI controller with advanced memory strategies
Icon Enhance GenAISys with multi-domain, multimodal reasoning capabilities and Chain of Thought (CoT)
Icon Seamlessly integrate cutting-edge OpenAI and DeepSeek models as you see fit
Defining a Business-Ready Generative AI System Chevron down icon Chevron up icon
Building the Generative AI Controller Chevron down icon Chevron up icon
Integrating Dynamic RAG into the GenAISys Chevron down icon Chevron up icon
Building the AI Controller Orchestration Interface Chevron down icon Chevron up icon
Adding Multimodal, Multifunctional Reasoning with Chain of Thought Chevron down icon Chevron up icon
Reasoning E-Marketing AI Agents Chevron down icon Chevron up icon
Enhancing the GenAISys with DeepSeek Chevron down icon Chevron up icon
GenAISys for Trajectory Simulation and Prediction Chevron down icon Chevron up icon
Upgrading the GenAISys with Data Security and Moderation for Customer Service Chevron down icon Chevron up icon
Presenting Your Business-Ready Generative AI System Chevron down icon Chevron up icon
Answers Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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C++ in Embedded Systems
C++ in Embedded Systems
Full star icon Full star icon Full star icon Full star icon Full star icon 5
By Amar Mahmutbegović
July 2025 | 402 pages
Icon Bridge the gap between C and modern C++ for embedded systems through practical examples
Icon Learn how to save memory and cut down on runtime computing using compile-time computation techniques
Icon Improve your software design skills by applying patterns to solve common problems in embedded systems using C++
Icon Purchase of the print or Kindle book includes a free PDF eBook
Part I: Introduction to C++ in Embedded Development Chevron down icon Chevron up icon
Debunking Common Myths about C++ Chevron down icon Chevron up icon
Challenges in Embedded Systems with Limited Resources Chevron down icon Chevron up icon
Embedded C++ Ecosystem Chevron down icon Chevron up icon
Setting Up the Development Environment for a C++ Embedded Project Chevron down icon Chevron up icon
Part II: C++ Fundamentals Chevron down icon Chevron up icon
Classes – Building Blocks of C++ Applications Chevron down icon Chevron up icon
Beyond Classes – Fundamental C++ Concepts Chevron down icon Chevron up icon
Strengthening Firmware – Practical C++ Error Handling Methods Chevron down icon Chevron up icon
Part III: C++ Advanced Concepts Chevron down icon Chevron up icon
Building Generic and Reusable Code with Templates Chevron down icon Chevron up icon
Improving Type-Safety with Strong Types Chevron down icon Chevron up icon
Writing Expressive Code with Lambdas Chevron down icon Chevron up icon
Compile-Time Computation Chevron down icon Chevron up icon
Part IV: Applying C++ to Solving Embedded Domain Problems Chevron down icon Chevron up icon
Writing C++ HAL Chevron down icon Chevron up icon
Working with C Libraries Chevron down icon Chevron up icon
Enhancing Super-Loop with Sequencer Chevron down icon Chevron up icon
Practical Patterns – Building a Temperature Publisher Chevron down icon Chevron up icon
Designing Scalable Finite State Machines Chevron down icon Chevron up icon
Libraries and Frameworks Chevron down icon Chevron up icon
Cross-Platform Development Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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Python Machine Learning By Example
Python Machine Learning By Example
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
By Yuxi (Hayden) Liu
July 2024 | 526 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
Getting Started with Machine Learning and Python Chevron down icon Chevron up icon
Building a Movie Recommendation Engine with Naïve Bayes Chevron down icon Chevron up icon
Predicting Online Ad Click-Through with Tree-Based Algorithms Chevron down icon Chevron up icon
Predicting Online Ad Click-Through with Logistic Regression Chevron down icon Chevron up icon
Predicting Stock Prices with Regression Algorithms Chevron down icon Chevron up icon
Predicting Stock Prices with Artificial Neural Networks Chevron down icon Chevron up icon
Mining the 20 Newsgroups Dataset with Text Analysis Techniques Chevron down icon Chevron up icon
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling Chevron down icon Chevron up icon
Recognizing Faces with Support Vector Machine Chevron down icon Chevron up icon
Machine Learning Best Practices Chevron down icon Chevron up icon
Categorizing Images of Clothing with Convolutional Neural Networks Chevron down icon Chevron up icon
Making Predictions with Sequences Using Recurrent Neural Networks Chevron down icon Chevron up icon
Advancing Language Understanding and Generation with the Transformer Models Chevron down icon Chevron up icon
Building an Image Search Engine Using CLIP: a Multimodal Approach Chevron down icon Chevron up icon
Making Decisions in Complex Environments with Reinforcement Learning Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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Master AI Agents: Build Powerful AI Agents and Generate Leads from Scratch
Master AI Agents: Build Powerful AI Agents and Generate Leads from Scratch
By Clarian North
July 2025 | 642 pages
Icon Comprehensive AI agent workflows using N8N and LLM integration
Icon Hands-on project building email and WhatsApp AI agents
Icon In-depth exploration of advanced automation and AI tools for business
Learn About What Are AI Agents - For Beginners - The Essentials Chevron down icon Chevron up icon
Asking LLMs Like DeepSeek to Solve Problems for You Chevron down icon Chevron up icon
High-Level Overview of Our AI Agents and Email Personal Assistant Chevron down icon Chevron up icon
What Is N8N - Our Advanced Level AI Agent Building Tool Chevron down icon Chevron up icon
Building an Email Organizing AI Agent Automation Chevron down icon Chevron up icon
Authorizations for AI Agents with Businesses - Facebook and WhatsApp Chevron down icon Chevron up icon
Building Our AI Agent from Scratch - Memory and LLM Integration Chevron down icon Chevron up icon
Advanced - Adding Voice Integration and Custom HTTP Requests with WhatsApp AI Chevron down icon Chevron up icon
Integrating Email Actions for a WhatsApp AI Agent Chevron down icon Chevron up icon
Multi AI Agents and Sub-Workflows in N8N for Our Email AI Agent Assistant Chevron down icon Chevron up icon
An Overview and Live Example of the AI Agent Lead Generator Blueprint Chevron down icon Chevron up icon
Fundamental Data Flow for AI Agent Design - LLM Systems and APIs Chevron down icon Chevron up icon
Building the Lead Generating Chat AI Agent Model Chevron down icon Chevron up icon
Building the AI Agent Tool for Browser Searching and Database Updates Chevron down icon Chevron up icon
Linking Multiple Agentic Workflows Together to Build a Company of Agents Chevron down icon Chevron up icon
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C# 13 and .NET 9 – Modern Cross-Platform Development Fundamentals
C# 13 and .NET 9 – Modern Cross-Platform Development Fundamentals
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
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
Speaking C# Chevron down icon Chevron up icon
Controlling Flow, Converting Types, and Handling Exceptions Chevron down icon Chevron up icon
Writing, Debugging, and Testing Functions Chevron down icon Chevron up icon
Building Your Own Types with Object-Oriented Programming Chevron down icon Chevron up icon
Implementing Interfaces and Inheriting Classes Chevron down icon Chevron up icon
Packaging and Distributing .NET Types Chevron down icon Chevron up icon
Working with Common .NET Types Chevron down icon Chevron up icon
Working with Files, Streams, and Serialization Chevron down icon Chevron up icon
Working with Data Using Entity Framework Core Chevron down icon Chevron up icon
Querying and Manipulating Data Using LINQ Chevron down icon Chevron up icon
Introducing Modern Web Development Using .NET Chevron down icon Chevron up icon
Building Websites Using ASP.NET Core Chevron down icon Chevron up icon
Building Interactive Web Components Using Blazor Chevron down icon Chevron up icon
Building and Consuming Web Services Chevron down icon Chevron up icon
Epilogue Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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LLM Design Patterns
LLM Design Patterns
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
By Ken Huang
May 2025 | 534 pages
Icon Learn comprehensive LLM development, including data prep, training pipelines, and optimization
Icon Explore advanced prompting techniques, such as chain-of-thought, tree-of-thought, RAG, and AI agents
Icon Implement evaluation metrics, interpretability, and bias detection for fair, reliable models
Icon Print or Kindle purchase includes a free PDF eBook
Part 1: Introduction and Data Preparation Chevron down icon Chevron up icon
Chapter 1: Introduction to LLM Design Patterns Chevron down icon Chevron up icon
Chapter 2: Data Cleaning for LLM Training Chevron down icon Chevron up icon
Chapter 3: Data Augmentation Chevron down icon Chevron up icon
Chapter 4: Handling Large Datasets for LLM Training Chevron down icon Chevron up icon
Chapter 5: Data Versioning Chevron down icon Chevron up icon
Chapter 6: Dataset Annotation and Labeling Chevron down icon Chevron up icon
Part 2: Training and Optimization of Large Language Models Chevron down icon Chevron up icon
Chapter 7: Training Pipeline Chevron down icon Chevron up icon
Chapter 8: Hyperparameter Tuning Chevron down icon Chevron up icon
Chapter 9: Regularization Chevron down icon Chevron up icon
Chapter 10: Checkpointing and Recovery Chevron down icon Chevron up icon
Chapter 11: Fine-Tuning Chevron down icon Chevron up icon
Chapter 12: Model Pruning Chevron down icon Chevron up icon
Chapter 13: Quantization Chevron down icon Chevron up icon
Part 3: Evaluation and Interpretation of Large Language Models Chevron down icon Chevron up icon
Chapter 14: Evaluation Metrics Chevron down icon Chevron up icon
Chapter 15: Cross-Validation Chevron down icon Chevron up icon
Chapter 16: Interpretability Chevron down icon Chevron up icon
Chapter 17: Fairness and Bias Detection Chevron down icon Chevron up icon
Chapter 18: Adversarial Robustness Chevron down icon Chevron up icon
Chapter 19: Reinforcement Learning from Human Feedback Chevron down icon Chevron up icon
Part 4: Advanced Prompt Engineering Techniques Chevron down icon Chevron up icon
Chapter 20: Chain-of-Thought Prompting Chevron down icon Chevron up icon
Chapter 21: Tree-of-Thoughts Prompting Chevron down icon Chevron up icon
Chapter 22: Reasoning and Acting Chevron down icon Chevron up icon
Chapter 23: Reasoning WithOut Observation Chevron down icon Chevron up icon
Chapter 24: Reflection Techniques Chevron down icon Chevron up icon
Chapter 25: Automatic Multi-Step Reasoning and Tool Use Chevron down icon Chevron up icon
Part 5: Retrieval and Knowledge Integration in Large Language Models Chevron down icon Chevron up icon
Chapter 26: Retrieval-Augmented Generation Chevron down icon Chevron up icon
Chapter 27: Graph-Based RAG Chevron down icon Chevron up icon
Chapter 28: Advanced RAG Chevron down icon Chevron up icon
Chapter 29: Evaluating RAG Systems Chevron down icon Chevron up icon
Chapter 30: Agentic Patterns Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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Solutions Architect's Handbook
Solutions Architect's Handbook
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
By Saurabh Shrivastava
March 2024 | 582 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
Principles of Solution Architecture Design Chevron down icon Chevron up icon
Cloud Migration and Cloud Architecture Design Chevron down icon Chevron up icon
Solution Architecture Design Patterns Chevron down icon Chevron up icon
Cloud-Native Architecture Design Patterns Chevron down icon Chevron up icon
Performance Considerations Chevron down icon Chevron up icon
Security Considerations Chevron down icon Chevron up icon
Architectural Reliability Considerations Chevron down icon Chevron up icon
Operational Excellence Considerations Chevron down icon Chevron up icon
Cost Considerations Chevron down icon Chevron up icon
DevOps and Solution Architecture Framework Chevron down icon Chevron up icon
Data Engineering for Solution Architecture Chevron down icon Chevron up icon
Machine Learning Architecture Chevron down icon Chevron up icon
Generative AI Architecture Chevron down icon Chevron up icon
Rearchitecting Legacy Systems Chevron down icon Chevron up icon
Solution Architecture Document Chevron down icon Chevron up icon
Learning Soft Skills to Become a Better Solutions Architect Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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GitHub Copilot Complete Guide for Developers
GitHub Copilot Complete Guide for Developers
By HHN Automate Book Inc.
July 2025 | 349 pages
Icon Comprehensive GitHub Copilot setup and feature exploration
Icon Advanced conversational workflows with Copilot Chat and slash commands
Icon Personalization with custom instructions, agents, and GitHub Copilot CLI
Introduction to GitHub Copilot Chevron down icon Chevron up icon
GitHub Copilot Primer and Environment Setup Chevron down icon Chevron up icon
Day 1 – Foundations & Inline Code in GitHub Copilot Chevron down icon Chevron up icon
Day 2 – Conversational Workflows in GitHub Copilot Chevron down icon Chevron up icon
Day 3 – Project-Wide Intelligence in GitHub Copilot Chevron down icon Chevron up icon
Day 4 – Developer Collaboration in GitHub Copilot Chevron down icon Chevron up icon
Day 5 – Code Edits & Personalization in GitHub Copilot Chevron down icon Chevron up icon
Day 6 – Agents, Tools, & Extensions in GitHub Copilot Chevron down icon Chevron up icon
Conclusion Chevron down icon Chevron up icon
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