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Mathematics of Machine Learning
Mathematics of Machine Learning
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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
Introduction What is this book about? How to read this book Conventions used What this book covers To get the most out of this book
Part 1: Linear Algebra Chevron down icon Chevron up icon
Part 1: Linear Algebra
1 Vectors and Vector Spaces Chevron down icon Chevron up icon
1 Vectors and Vector Spaces 1.1 What is a vector space? 1.2 The basis 1.3 Vectors in practice 1.4 Summary 1.5 Problems
2 The Geometric Structure of Vector Spaces Chevron down icon Chevron up icon
2 The Geometric Structure of Vector Spaces 2.1 Norms and distances 2.2 Inner products, angles, and lots of reasons to care about them 2.3 Summary 2.4 Problems
3 Linear Algebra in Practice Chevron down icon Chevron up icon
3 Linear Algebra in Practice 3.1 Vectors in NumPy 3.2 Matrices, the workhorses of linear algebra 3.3 Summary 3.4 Problems
4 Linear Transformations Chevron down icon Chevron up icon
4 Linear Transformations 4.1 What is a linear transformation? 4.2 Change of basis 4.3 Linear transformations in the Euclidean plane 4.4 Determinants, or how linear transformations affect volume 4.5 Summary 4.6 Problems
5 Matrices and Equations Chevron down icon Chevron up icon
5 Matrices and Equations 5.1 Linear equations 5.2 The LU decomposition 5.3 Determinants in practice 5.4 Summary 5.5 Problems
6 Eigenvalues and Eigenvectors Chevron down icon Chevron up icon
6 Eigenvalues and Eigenvectors 6.1 Eigenvalues of matrices 6.2 Finding eigenvalue-eigenvector pairs 6.3 Eigenvectors, eigenspaces, and their bases 6.4 Summary 6.5 Problems
7 Matrix Factorizations Chevron down icon Chevron up icon
7 Matrix Factorizations 7.1 Special transformations 7.2 Self-adjoint transformations and the spectral decomposition theorem 7.3 The singular value decomposition 7.4 Orthogonal projections 7.5 Computing eigenvalues 7.6 The QR algorithm 7.7 Summary 7.8 Problems
8 Matrices and Graphs Chevron down icon Chevron up icon
8 Matrices and Graphs 8.1 The directed graph of a nonnegative matrix 8.2 Benefits of the graph representation 8.3 The Frobenius normal form 8.4 Summary 8.5 Problems
References Chevron down icon Chevron up icon
References
Part 2: Calculus Chevron down icon Chevron up icon
Part 2: Calculus
9 Functions Chevron down icon Chevron up icon
9 Functions 9.1 Functions in theory 9.2 Functions in practice 9.3 Summary 9.4 Problems
10 Numbers, Sequences, and Series Chevron down icon Chevron up icon
10 Numbers, Sequences, and Series 10.1 Numbers 10.2 Sequences 10.3 Series 10.4 Summary 10.5 Problems
11 Topology, Limits, and Continuity Chevron down icon Chevron up icon
11 Topology, Limits, and Continuity 11.1 Topology 11.2 Limits 11.3 Continuity 11.4 Summary 11.5 Problems
12 Differentiation Chevron down icon Chevron up icon
12 Differentiation 12.1 Differentiation in theory 12.2 Differentiation in practice 12.3 Summary 12.4 Problems
13 Optimization Chevron down icon Chevron up icon
13 Optimization 13.1 Minima, maxima, and derivatives 13.2 The basics of gradient descent 13.3 Why does gradient descent work? 13.4 Summary 13.5 Problems
14 Integration Chevron down icon Chevron up icon
14 Integration 14.1 Integration in theory 14.2 Integration in practice 14.3 Summary 14.4 Problems Join our community on Discord
References Chevron down icon Chevron up icon
References
Part 3: Multivariable Calculus Chevron down icon Chevron up icon
Part 3: Multivariable Calculus
15 Multivariable Functions Chevron down icon Chevron up icon
15 Multivariable Functions 15.1 What is a multivariable function? 15.2 Linear functions in multiple variables 15.3 The curse of dimensionality 15.4 Summary
16 Derivatives and Gradients Chevron down icon Chevron up icon
16 Derivatives and Gradients 16.1 Partial and total derivatives 16.2 Derivatives of vector-valued functions 16.3 Summary 16.4 Problems
17 Optimization in Multiple Variables Chevron down icon Chevron up icon
17 Optimization in Multiple Variables 17.1 Multivariable functions in code 17.2 Minima and maxima, revisited 17.3 Gradient descent in its full form 17.4 Summary 17.5 Problems
References Chevron down icon Chevron up icon
References
Part 4: Probability Theory Chevron down icon Chevron up icon
Part 4: Probability Theory
18 What is Probability? Chevron down icon Chevron up icon
18 What is Probability? 18.1 The language of thinking 18.2 The axioms of probability 18.3 Conditional probability 18.4 Summary 18.5 Problems
19 Random Variables and Distributions Chevron down icon Chevron up icon
19 Random Variables and Distributions 19.1 Random variables 19.2 Discrete distributions 19.3 Real-valued distributions 19.4 Density functions 19.5 Summary 19.6 Problems
20 The Expected Value Chevron down icon Chevron up icon
20 The Expected Value 20.1 Discrete random variables 20.2 Continuous random variables 20.3 Properties of the expected value 20.4 Variance 20.5 The law of large numbers 20.6 Information theory 20.7 The Maximum Likelihood Estimation 20.8 Summary 20.9 Problems
References Chevron down icon Chevron up icon
References
Part 5: Appendix Chevron down icon Chevron up icon
Part 5: Appendix
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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Building AI Agents with LLMs, RAG, and Knowledge Graphs
Building AI Agents with LLMs, RAG, and Knowledge Graphs
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8
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
Part 1: The AI Agent Engine: From Text to Large Language Models
Chapter 1: Analyzing Text Data with Deep Learning Chevron down icon Chevron up icon
Chapter 1: Analyzing Text Data with Deep Learning Technical requirements Representing text for AI Embedding, application, and representation RNNs, LSTMs, GRUs, and CNNs for text Performing sentiment analysis with embedding and deep learning Summary
Chapter 2: The Transformer: The Model Behind the Modern AI Revolution Chevron down icon Chevron up icon
Chapter 2: The Transformer: The Model Behind the Modern AI Revolution Technical requirements Exploring attention and self-attention Introducing the transformer model Training a transformer Exploring masked language modeling Visualizing internal mechanisms Applying a transformer Summary
Chapter 3: Exploring LLMs as a Powerful AI Engine Chevron down icon Chevron up icon
Chapter 3: Exploring LLMs as a Powerful AI Engine Technical requirements Discovering the evolution of LLMs Instruction tuning, fine-tuning, and alignment Exploring smaller and more efficient LLMs Exploring multimodal models Understanding hallucinations and ethical and legal issues Prompt engineering Summary Further reading
Part 2: AI Agents and Retrieval of Knowledge Chevron down icon Chevron up icon
Part 2: AI Agents and Retrieval of Knowledge
Chapter 4: Building a Web Scraping Agent with an LLM Chevron down icon Chevron up icon
Chapter 4: Building a Web Scraping Agent with an LLM Technical requirements Understanding the brain, perception, and action paradigm Classifying AI agents Understanding the abilities of single-agent and multiple-agent systems Exploring the principal libraries Creating an agent to search the web Summary Further reading
Chapter 5: Extending Your Agent with RAG to Prevent Hallucinations Chevron down icon Chevron up icon
Chapter 5: Extending Your Agent with RAG to Prevent Hallucinations Technical requirements Exploring naïve RAG Retrieval, optimization, and augmentation Evaluating the output Comparison between RAG and fine-tuning Using RAG to build a movie recommendation agent Summary Further reading
Chapter 6: Advanced RAG Techniques for Information Retrieval and Augmentation Chevron down icon Chevron up icon
Chapter 6: Advanced RAG Techniques for Information Retrieval and Augmentation Technical requirements Discussing naïve RAG issues Exploring the advanced RAG pipeline Modular RAG and its integration with other systems Implementing an advanced RAG pipeline Understanding the scalability and performance of RAG Open questions and future perspectives Summary Further reading
Chapter 7: Creating and Connecting a Knowledge Graph to an AI Agent Chevron down icon Chevron up icon
Chapter 7: Creating and Connecting a Knowledge Graph to an AI Agent Technical requirements Introduction to knowledge graphs Creating a knowledge graph with your LLM Retrieving information with a knowledge graph and an LLM Understanding graph reasoning Ongoing challenges in knowledge graphs and GraphRAG Summary Further reading
Chapter 8: Reinforcement Learning and AI Agents Chevron down icon Chevron up icon
Chapter 8: Reinforcement Learning and AI Agents Technical requirements Introduction to reinforcement learning Deep reinforcement learning LLM interactions with RL models Key takeaways Summary Further reading
Part 3: Creating Sophisticated AI to Solve Complex Scenarios Chevron down icon Chevron up icon
Part 3: Creating Sophisticated AI to Solve Complex Scenarios
Chapter 9: Creating Single- and Multi-Agent Systems Chevron down icon Chevron up icon
Chapter 9: Creating Single- and Multi-Agent Systems Technical requirements Introduction to autonomous agents Working with HuggingGPT Multi-agent system SaaS, MaaS, DaaS, and RaaS Summary Further reading
Chapter 10: Building an AI Agent Application Chevron down icon Chevron up icon
Chapter 10: Building an AI Agent Application Technical requirements Introduction to Streamlit Developing our frontend with Streamlit Creating an application with Streamlit and AI agents Machine learning operations and LLM operations Asynchronous programming Docker Summary Further reading
Chapter 11: The Future Ahead Chevron down icon Chevron up icon
Chapter 11: The Future Ahead AI agents in healthcare AI agents in other sectors Challenges and open questions Summary Further reading
Index Chevron down icon Chevron up icon
Index Why subscribe?
Other Books You May Enjoy Chevron down icon Chevron up icon
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Building Agentic AI Systems
Building Agentic AI Systems
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Full star icon Full star icon Full star icon Full star icon Empty star icon 4
By Anjanava Biswas
April 2025 | 292 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
Part 1: Foundations of Generative AI and Agentic Systems Chevron down icon Chevron up icon
Part 1: Foundations of Generative AI and Agentic Systems
Chapter 1: Fundamentals of Generative AI Chevron down icon Chevron up icon
Chapter 1: Fundamentals of Generative AI Introduction to generative AI Types of generative AI models Applications of generative AI Challenges and limitations of generative AI Summary Questions Answers Further reading References
Chapter 2: Principles of Agentic Systems Chevron down icon Chevron up icon
Chapter 2: Principles of Agentic Systems Technical requirements Understanding self-governance, agency, and autonomy Reviewing intelligent agents and their characteristics Exploring the architecture of agentic systems Understanding multi-agent systems Summary Questions Answers Subscribe to AI_Distilled, the go-to newsletter for AI professionals
Chapter 3: Essential Components of Intelligent Agents Chevron down icon Chevron up icon
Chapter 3: Essential Components of Intelligent Agents Technical requirements Knowledge representation in intelligent agents Reasoning in intelligent agents Learning mechanisms for adaptive agents Decision-making and planning in agentic systems Enhancing agent capabilities with generative AI Summary Questions Answers
Part 2: Designing and Implementing Generative AI-Based Agents Chevron down icon Chevron up icon
Part 2: Designing and Implementing Generative AI-Based Agents
Chapter 4: Reflection and Introspection in Agents Chevron down icon Chevron up icon
Chapter 4: Reflection and Introspection in Agents Technical requirements The importance of reflection in agents Introspection in intelligent agents Implementing reflective capabilities Use cases and examples Summary Questions Answers Subscribe to AI_Distilled, the go-to newsletter for AI professionals
Chapter 5: Enabling Tool Use and Planning in Agents Chevron down icon Chevron up icon
Chapter 5: Enabling Tool Use and Planning in Agents Technical requirements Understanding the concept of tool use in agents Planning algorithms for agents Integrating tool use and planning Exploring practical implementations Summary Questions Answers
Chapter 6: Exploring the Coordinator, Worker, and Delegator Approach Chevron down icon Chevron up icon
Chapter 6: Exploring the Coordinator, Worker, and Delegator Approach Technical requirements Understanding the CWD model Designing agents with role assignments Communication and collaboration between agents Implementing the CWD approach in generative AI systems Summary Questions Answers Subscribe to AI_Distilled, the go-to newsletter for AI professionals
Chapter 7: Effective Agentic System Design Techniques Chevron down icon Chevron up icon
Chapter 7: Effective Agentic System Design Techniques Technical requirements Focused system prompts and instructions for agents State spaces and environment modeling Agent memory architecture and context management Sequential and parallel processing in agentic workflows Summary Questions Answers
Part 3: Trust, Safety, Ethics, and Applications Chevron down icon Chevron up icon
Part 3: Trust, Safety, Ethics, and Applications
Chapter 8: Building Trust in Generative AI Systems Chevron down icon Chevron up icon
Chapter 8: Building Trust in Generative AI Systems Technical requirements Importance of trust in AI Techniques for establishing trust Implementing transparency and explainability Handling uncertainty and biases Summary Questions Answers Subscribe to AI_Distilled, the go-to newsletter for AI professionals
Chapter 9: Managing Safety and Ethical Considerations Chevron down icon Chevron up icon
Chapter 9: Managing Safety and Ethical Considerations Understanding potential risks and challenges Ensuring safe and responsible AI Exploring ethical guidelines and frameworks Addressing privacy and security concerns Summary Questions Answers
Chapter 10: Common Use Cases and Applications Chevron down icon Chevron up icon
Chapter 10: Common Use Cases and Applications Creative and artistic applications Natural language processing and conversational agents Robotics and autonomous systems Decision support and optimization Summary Questions Answers Subscribe to AI_Distilled, the go-to newsletter for AI professionals
Chapter 11: Conclusion and Future Outlook Chevron down icon Chevron up icon
Chapter 11: Conclusion and Future Outlook Recap of key concepts Emerging trends and research directions Artificial general intelligence Challenges and opportunities Summary
Chapter 12: Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Chapter 12: Unlock Your Exclusive Benefits Unlock this Book’s Free Benefits in 3 Easy Steps
Index Chevron down icon Chevron up icon
Index Why subscribe?
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy Packt is searching for authors like you Share Your Thoughts Join our community on Discord Subscribe to AI_Distilled, the go-to newsletter for AI professionals
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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 4.9
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
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 Join our book’s Discord space
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 Join our book’s Discord space
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 Join our book’s Discord space
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 Join our book’s Discord space
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 Join our book’s Discord space
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
MLOps Principles Chevron down icon Chevron up icon
MLOps Principles 1. Automation or operationalization 2. Versioning 3. Experiment tracking 4. Testing 5. Monitoring 6. Reproducibility
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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Building Agents with OpenAI Agents SDK
Building Agents with OpenAI Agents SDK
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By Habib
October 2025 | 276 pages
Icon Gain a complete understanding of the OpenAI Agents SDK features including models, tools, memory, guardrails, orchestration, tracing, and multi-agent systems
Icon Progressively build AI agents through several hands-on projects that evolve from a simple workflow to a complex multi-agent system
Icon Implement advanced agent capabilities such as RAG, MCPs, administration, workflow integration, and much more
Part 1: AI Agents Chevron down icon Chevron up icon
Part 1: AI Agents
Introduction to AI Agents Chevron down icon Chevron up icon
Introduction to AI Agents Technical requirements Overview of AI agents Practical applications of AI agents Build methodology of AI agents Summary
Introduction to OpenAI Agents SDK Chevron down icon Chevron up icon
Introduction to OpenAI Agents SDK Technical requirements Design features of OpenAI Agents SDK Core primitives Summary Subscribe for a free eBook
Environment Setup and Developing Your First Agent Chevron down icon Chevron up icon
Environment Setup and Developing Your First Agent Technical requirements Environment setup Development prerequisites Developing your first AI Agent Summary
Part 2: OpenAI Agents SDK Chevron down icon Chevron up icon
Part 2: OpenAI Agents SDK
Agent Tools and MCPs Chevron down icon Chevron up icon
Agent Tools and MCPs Technical requirements Using custom tools with Python functions OpenAI hosted tools MCP Summary Subscribe for a free eBook
Memory and Knowledge Chevron down icon Chevron up icon
Memory and Knowledge Technical requirements Working memory Long-term memory Training knowledge Retrieved knowledge Summary
Multi-Agent Systems and Handoffs Chevron down icon Chevron up icon
Multi-Agent Systems and Handoffs Technical requirements Multi-agent orchestrations Handoffs in OpenAI Agents SDK Multi-agent patterns Summary Subscribe for a free eBook
Model and Context Management Chevron down icon Chevron up icon
Model and Context Management Technical requirements Model management Context management Summary
Agent System Management Chevron down icon Chevron up icon
Agent System Management Technical requirements Agent visualization Guardrails Logging, tracing, and observability Agent testing Summary Subscribe for a free eBook
Part 3: Build AI Agents Chevron down icon Chevron up icon
Part 3: Build AI Agents
Building AI Agents and Agentic Systems Chevron down icon Chevron up icon
Building AI Agents and Agentic Systems Technical requirements Building a customer service employee AI agent Orchestrating an automated multi-agent workflow 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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Node.js Design Patterns
Node.js Design Patterns
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Full star icon Full star icon Full star icon Full star icon Full star icon 5
By Luciano Mammino
September 2025 | 732 pages
Icon Gain a deep understanding of the Node.js philosophy, its core components, and the solutions in its ecosystem
Icon Avoid common pitfalls in applying proven patterns to create robust, maintainable Node.js applications
Icon Enhance your development skills through a wealth of real-world examples and case studies
The Node.js Platform Chevron down icon Chevron up icon
The Node.js Platform The Node.js philosophy How Node.js works JavaScript in Node.js Summary
The Module System Chevron down icon Chevron up icon
The Module System The need for modules Module systems in JavaScript and Node.js The revealing module pattern ES modules CommonJS modules ES modules and CommonJS—differences and interoperability Using modules in TypeScript Summary
Callbacks and Events Chevron down icon Chevron up icon
Callbacks and Events The Callback pattern The Observer pattern EventEmitter versus callbacks Combining callbacks and events Summary Exercises
Asynchronous Control Flow Patterns with Callbacks Chevron down icon Chevron up icon
Asynchronous Control Flow Patterns with Callbacks The challenges of asynchronous programming Callback best practices Control flow patterns Summary Exercises
Asynchronous Control Flow Patterns with Promises and Async/Await Chevron down icon Chevron up icon
Asynchronous Control Flow Patterns with Promises and Async/Await Promises Async/await The problem with infinite recursive promise resolution chains Summary Exercises
Coding with Streams Chevron down icon Chevron up icon
Coding with Streams Discovering the importance of streams Getting started with streams Asynchronous control flow patterns with streams Piping patterns Readable stream utilities Web Streams Stream consumer utilities Summary Exercises
Creational Design Patterns Chevron down icon Chevron up icon
Creational Design Patterns Factory Builder Revealing Constructor Singleton Wiring modules Summary Exercises
Structural Design Patterns Chevron down icon Chevron up icon
Structural Design Patterns Proxy Decorator The line between Proxy and Decorator Adapter Summary Exercises
Behavioral Design Patterns Chevron down icon Chevron up icon
Behavioral Design Patterns Strategy State Template Iterator Middleware Command Summary Exercises
Testing: Patterns and Best Practices Chevron down icon Chevron up icon
Testing: Patterns and Best Practices An introduction to software testing Writing tests with Node.js The Node.js test runner Writing unit tests Writing integration tests Writing E2E tests Summary Exercises
Advanced Recipes Chevron down icon Chevron up icon
Advanced Recipes Dealing with asynchronously initialized components Asynchronous request batching and caching Canceling asynchronous operations Running CPU-bound tasks Summary Exercises
Scalability and Architectural Patterns Chevron down icon Chevron up icon
Scalability and Architectural Patterns An introduction to application scaling Cloning and load balancing Decomposing complex applications Summary Exercises
Messaging and Integration Patterns Chevron down icon Chevron up icon
Messaging and Integration Patterns Fundamentals of a messaging system Publish/Subscribe pattern Task distribution patterns Request/Reply patterns Summary Exercises
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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Hands-On RTOS with Microcontrollers
Hands-On RTOS with Microcontrollers
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Full star icon Full star icon Full star icon Full star icon Full star icon 5
By Jim Yuill
September 2025 | 570 pages
Icon Understand and master RTOS concepts using the powerful STM32 platform
Icon Strengthen your embedded programming skills for real-world applications
Icon Explore advanced RTOS techniques to unlock innovative embedded solutions
Icon All formats include a free PDF and an invitation to the Embedded System Professionals community
Part 1: Introduction to RTOSes, the Dev-Board, and Tools Chevron down icon Chevron up icon
Part 1: Introduction to RTOSes, the Dev-Board, and Tools
Introducing Real-Time Systems Chevron down icon Chevron up icon
Introducing Real-Time Systems What is “real-time” anyway? Hardware and software roles in real-time systems Defining RTOSes Practical real-time systems Choosing an RTOS Summary
Introducing the Development Board Chevron down icon Chevron up icon
Introducing the Development Board Dev-board overview Understanding the dev-board’s components Understanding the hardware documentation Summary
Introducing the Development-Tools Chevron down icon Chevron up icon
Introducing the Development-Tools Using the book’s instructions and example-programs The IDE and the example-programs The debugging tools: J-Link, Ozone and SystemView Summary
Understanding Super-Loops Chevron down icon Chevron up icon
Understanding Super-Loops Introducing super-loops Improving the super-loop with interrupts Adding a timer to the super-loop Summary
Implementing the Super-Loop Chevron down icon Chevron up icon
Implementing the Super-Loop The super-loop example-program Learning about system APIs and hardware features How to develop the super-loop program Summary
Part 2: The FreeRTOS Scheduler, Tasks, and Primary APIs Chevron down icon Chevron up icon
Part 2: The FreeRTOS Scheduler, Tasks, and Primary APIs
Understanding RTOS Tasks Chevron down icon Chevron up icon
Understanding RTOS Tasks The FreeRTOS scheduler and tasks: a conceptual view Choosing FreeRTOS as our RTOS Hands-on with FreeRTOS’s scheduler and tasks Summary
Running the FreeRTOS Scheduler Chevron down icon Chevron up icon
Running the FreeRTOS Scheduler Creating tasks and starting the scheduler Deleting tasks Trying out the code Understanding the scheduler Task implementation and troubleshooting Summary
Protecting Data and Synchronizing Tasks Chevron down icon Chevron up icon
Protecting Data and Synchronizing Tasks Running the chapter’s example-programs Semaphores Priority inversion (how not to use semaphores) Mutexes Race conditions Software timers Summary Further reading
Intertask Communication Chevron down icon Chevron up icon
Intertask Communication Understanding RTOS queues Passing data through queues by value Passing data through queues by reference Task notifications Summary Further reading
Part 3: Using FreeRTOS to Build Systems Chevron down icon Chevron up icon
Part 3: Using FreeRTOS to Build Systems
Drivers and ISRs Chevron down icon Chevron up icon
Drivers and ISRs Introducing the UART Creating a polled UART driver FreeRTOS-tasks vs. ISRs Creating ISR-based drivers Summary Further reading
More Efficient Drivers and ISRs Chevron down icon Chevron up icon
More Efficient Drivers and ISRs Creating a basic DMA-based driver Creating an enhanced DMA-based driver Choosing a driver model Using third-party libraries and drivers Summary Further reading
Sharing Hardware Peripherals among Tasks Chevron down icon Chevron up icon
Sharing Hardware Peripherals among Tasks Understanding how messages are sent over USB Sending messages using a simple USB API Creating a VCP driver, using a stream-buffer Enhancing the VCP driver, for use by multiple tasks Summary Further reading
Creating Loose Coupling with Queues Chevron down icon Chevron up icon
Creating Loose Coupling with Queues Creating a command queue Reusing a queue definition for a new target Understanding queues as interfaces Summary Further reading
Part 4: System Design and Troubleshooting Chevron down icon Chevron up icon
Part 4: System Design and Troubleshooting
FreeRTOS Memory Management Chevron down icon Chevron up icon
FreeRTOS Memory Management Understanding memory allocation Static and dynamic allocation of FreeRTOS primitives FreeRTOS heap implementations Replacing malloc and free Monitoring for stack and heap problems Using a memory protection unit (MPU) Summary Further reading
Multi-Processor and Multi-Core Systems Chevron down icon Chevron up icon
Multi-Processor and Multi-Core Systems Introducing multi-core and multi-processor systems Exploring multi-core systems Exploring multi-processor systems Exploring inter-processor communication Choosing between multi-core and multi-processor systems Summary Further reading
Troubleshooting Tips and Next Steps Chevron down icon Chevron up icon
Troubleshooting Tips and Next Steps General FreeRTOS debugging-techniques Troubleshooting for the debugging tools Next steps
Part 5: Reference Information and FreeRTOS Installation Chevron down icon Chevron up icon
Part 5: Reference Information and FreeRTOS Installation
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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Generative AI with LangChain
Generative AI with LangChain
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Full star icon Full star icon Full star icon Full star icon Full star icon 5
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
The Rise of Generative AI: From Language Models to Agents The modern LLM landscape From models to agentic applications Introducing LangChain Summary Questions
First Steps with LangChain Chevron down icon Chevron up icon
First Steps with LangChain Setting up dependencies for this book Exploring LangChain’s building blocks Running local models Multimodal AI applications Summary Review questions Subscribe to our weekly newsletter
Building Workflows with LangGraph Chevron down icon Chevron up icon
Building Workflows with LangGraph LangGraph fundamentals Prompt engineering Working with short context windows Understanding memory mechanisms Summary Questions Subscribe to our weekly newsletter
Building Intelligent RAG Systems Chevron down icon Chevron up icon
Building Intelligent RAG Systems From indexes to intelligent retrieval Components of a RAG system From embeddings to search Breaking down the RAG pipeline Developing a corporate documentation chatbot Troubleshooting RAG systems Summary Questions Subscribe to our weekly newsletter
Building Intelligent Agents Chevron down icon Chevron up icon
Building Intelligent Agents What is a tool? Defining tools Advanced tool-calling capabilities Incorporating tools into workflows What are agents? Summary Questions Subscribe to our weekly newsletter
Advanced Applications and Multi-Agent Systems Chevron down icon Chevron up icon
Advanced Applications and Multi-Agent Systems Agentic architectures Multi-agent architectures Building adaptive systems Exploring reasoning paths Agent memory Summary Questions Subscribe to our weekly newsletter
Software Development and Data Analysis Agents Chevron down icon Chevron up icon
Software Development and Data Analysis Agents LLMs in software development Writing code with LLMs Applying LLM agents for data science Summary Questions
Evaluation and Testing Chevron down icon Chevron up icon
Evaluation and Testing Why evaluation matters What we evaluate: core agent capabilities How we evaluate: methodologies and approaches Evaluating LLM agents in practice Offline evaluation Summary Questions
Production-Ready LLM Deployment and Observability Chevron down icon Chevron up icon
Production-Ready LLM Deployment and Observability Security considerations for LLM applications Deploying LLM apps How to observe LLM apps Cost management for LangChain applications Summary Questions
The Future of Generative Models: Beyond Scaling Chevron down icon Chevron up icon
The Future of Generative Models: Beyond Scaling The current state of generative AI The limitations of scaling and emerging alternatives Economic and industry transformation Societal implications Summary Subscribe to our weekly newsletter
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
Index Chevron down icon Chevron up icon
Index Stay connected with Packt’s Generative AI community
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Learn Model Context Protocol with Python
Learn Model Context Protocol with Python
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By Christoffer Noring
October 2025 | 304 pages
Icon The only resource you'll need to build, test, and deploy MCP servers and clients
Icon Take a modern approach toward building, testing, and securing distributed agentic AI apps
Icon Get clear, professional guidance on developing for both LLM and non-LLM clients
Icon Purchase of the print or Kindle book includes a free PDF eBook
Introduction to the Model Context Protocol Chevron down icon Chevron up icon
Introduction to the Model Context Protocol Getting the most out of this book – get to know your free benefits How we got here, from SOAP to REST to GraphQL to gRPC to MCP The need for a standard Endless possibilities: know how to prompt, and a world of MCP servers is your oyster What is the MCP? Summary and next steps: now what?
Explaining the Model Context Protocol Chevron down icon Chevron up icon
Explaining the Model Context Protocol Learning about the protocol by implementing it Transports in MCP Summary Assignment Solution Quiz
Building and Testing Servers Chevron down icon Chevron up icon
Building and Testing Servers A STDIO server Concepts Runtimes Testing the server First server Summary Assignment – an e-commerce STDIO server Solution Quiz References
Building SSE Servers Chevron down icon Chevron up icon
Building SSE Servers SSE concepts Creating an SSE server as a web app Testing with SSE Creating an SSE server Summary Assignment – SSE server Solution Quiz References
Streamable HTTP Chevron down icon Chevron up icon
Streamable HTTP Streamable HTTP versus SSE, and why it is the new standard Streamable HTTP in MCP Resumability Notifications Creating and testing a server with Streamable HTTP Testing the server Testing out resumability Summary Assignment Solution Quiz References
Advanced Servers Chevron down icon Chevron up icon
Advanced Servers Why this low-level approach? Context managers Context managers in MCP servers Low-level access Organizing your architecture Summary Assignment Solution Quiz
Building Clients Chevron down icon Chevron up icon
Building Clients Building a client Exercise: Building a client Clients with LLMs Working with an LLM Exercise: Integrating the LLM Summary Assignment Solution Quiz
Consuming Servers Chevron down icon Chevron up icon
Consuming Servers Consuming with hosts such as Claude Desktop and VS Code Installation process Adding a server Local and global install Tips and tricks Security aspects Suggestion for servers Summary Assignment Quiz
Sampling Chevron down icon Chevron up icon
Sampling Why sampling? Implementing sampling Summary Assignment Solution Quiz
Elicitation Chevron down icon Chevron up icon
Elicitation Why elicitation? Implementing elicitation Elicitation flow JSON-RPC messages Implementing the server-side functionality Testing elicitation with VS Code Summary Assignment Solution Quiz
Securing Your Application Chevron down icon Chevron up icon
Securing Your Application Basic authentication Hardening security with JWT OAuth2 Summary Assignment 1: Secure your MCP server with basic authentication Solution 1 Assignment 2: Secure your MCP server with JWT Solution 2 Quiz Resources
Bringing MCP Apps to Production Chevron down icon Chevron up icon
Bringing MCP Apps to Production Architecture and design Packaging and distribution Testing and deployment automation Operations and observability Summary Resources
Unlock Your Book’s Exclusive Benefits Chevron down icon Chevron up icon
Unlock Your Book’s Exclusive Benefits How to unlock these benefits in three easy steps
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AI Agents in Practice
AI Agents in Practice
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Full star icon Full star icon Full star icon Full star icon Full star icon 5
By Valentina Alto
August 2025 | 282 pages
Icon Build production-ready AI agents with hands-on tutorials for diverse industry applications
Icon Explore multi-agent system architectures with practical frameworks for orchestrator comparison
Icon Future-proof your AI development with ethical implementation strategies and security patterns
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Part 1: Foundations of AI Workflows and the Rise of AI Agents Chevron down icon Chevron up icon
Part 1: Foundations of AI Workflows and the Rise of AI Agents
Evolution of GenAI Workflows Chevron down icon Chevron up icon
Evolution of GenAI Workflows Technical requirements Understanding foundation models and the rise of LLMs Latest significant breakthroughs Road to AI agents The need for an additional layer of intelligence: introducing AI agents Summary References
The Rise of AI Agents Chevron down icon Chevron up icon
The Rise of AI Agents Technical requirements Evolution of agents from RPA to AI agents Components of an AI agent Different types of AI agents Summary References Subscribe for a Free eBook
Part 2: Designing, Building, and Scaling AI Agents Chevron down icon Chevron up icon
Part 2: Designing, Building, and Scaling AI Agents
The Need for an AI Orchestrator Chevron down icon Chevron up icon
The Need for an AI Orchestrator Introduction to AI orchestrators Core components of an AI orchestrator Overview of the most popular AI orchestrators in the market How to choose the right orchestrator for your AI agent Summary References
The Need for Memory and Context Management Chevron down icon Chevron up icon
The Need for Memory and Context Management Different types of memory Managing context windows Storing, retrieving, and refreshing memory Popular tools to manage memory Summary References Subscribe for a Free eBook
The Need for Tools and External Integrations Chevron down icon Chevron up icon
The Need for Tools and External Integrations Technical requirements The anatomy of an AI agent’s tools Hardcoded and semantic functions APIs and web services Databases and knowledge bases Synchronous versus asynchronous calls Summary References
Building Your First AI Agent with LangChain Chevron down icon Chevron up icon
Building Your First AI Agent with LangChain Technical requirements Introduction to the LangChain ecosystem Overview of out-of-the-box components Use case – e-commerce AI agent Summary References Subscribe for a Free eBook
Multi-Agent Applications Chevron down icon Chevron up icon
Multi-Agent Applications Technical requirements Introduction to multi-agent systems Understanding and designing different workflows for your multi-agent system Overview of multi-agent orchestrators Building your first multi-agent application with LangGraph Summary References
Part 3: Road to an Open, Agentic Ecosystem Chevron down icon Chevron up icon
Part 3: Road to an Open, Agentic Ecosystem
Orchestrating Intelligence: Blueprint for Next-Gen Agent Protocols Chevron down icon Chevron up icon
Orchestrating Intelligence: Blueprint for Next-Gen Agent Protocols Technical requirements What is a protocol? Understanding the Model Context Protocol Agent2Agent Agent Commerce Protocol Toward an agentic web Summary References Subscribe for a Free eBook
Navigating Ethical Challenges in Real-World AI Chevron down icon Chevron up icon
Navigating Ethical Challenges in Real-World AI Ethical challenges in AI – fairness, transparency, privacy, and accountability Agentic AI autonomy and its unique ethical challenges Guardrails for safe and ethical AI Content filtering and moderation in AI systems Addressing the challenges: governance, regulations, and collaboration Summary References
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The Ultimate Ubuntu Handbook
The Ultimate Ubuntu Handbook
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By Ken VanDine
August 2025 | 356 pages
Icon Master Ubuntu 24.04 through a structured learning path, from initial setup and customization to advanced development workflows
Icon Avoid common mistakes with practical tips for ensuring stability, security, and clean configuration
Icon Learn directly from an Ubuntu Core developer as he shares his insider knowledge and best practices
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Part 1: Getting to Know Ubuntu Chevron down icon Chevron up icon
Part 1: Getting to Know Ubuntu
Introduction to Ubuntu Chevron down icon Chevron up icon
Introduction to Ubuntu A brief history of Ubuntu The Ubuntu mission – free software for everyone Ubuntu releases – trust through stability The power of community – collaboration and support Summary Further reading
What’s New in Ubuntu 24.04? Chevron down icon Chevron up icon
What’s New in Ubuntu 24.04? A fresh look and feel – desktop environment enhancements Under-the-hood improvements Security focus – enhanced protection Summary Further reading
Security and Transparency – The Advantages of Open Source Software Chevron down icon Chevron up icon
Security and Transparency – The Advantages of Open Source Software I am who I am because of who we all are What is free software? Many people make light work – the power of open source Transparency builds trust – a foundation for secure computing How to make your mark on the world A showcase of open source projects Summary Further reading
Getting Started with Ubuntu: A User’s Guide Chevron down icon Chevron up icon
Getting Started with Ubuntu: A User’s Guide Booting up – your first steps with Ubuntu Installation made easy – a guided process Ubuntu Welcome Essential hardware – installing necessary drivers Summary Further reading
Part 2: Getting the Most Out of Your Ubuntu System Chevron down icon Chevron up icon
Part 2: Getting the Most Out of Your Ubuntu System
Using Your Ubuntu Desktop Chevron down icon Chevron up icon
Using Your Ubuntu Desktop Mastering the desktop – your gateway to applications Unveiling the power of workspaces – multitasking made easy Introducing tiling window management Capturing screenshots and screen recording Controlling your desktop like a pro with keyboard shortcuts Working with files and folders – the power of file management The power of customization – tailoring your Ubuntu experience A tour of essential applications Summary Further reading
Software Discovery: Finding and Installing Applications Chevron down icon Chevron up icon
Software Discovery: Finding and Installing Applications Introducing Debian packages Introducing Snap packages Unveiling the Ubuntu App Center: your one-stop shop for applications Exploring the power of Snap The power of the command line: installing software with APT and Snap Advanced Snap concepts Summary Further reading
Software Updates: Enhancing Security and Stability Chevron down icon Chevron up icon
Software Updates: Enhancing Security and Stability The importance of updates: constantly vigilant Keeping your system updated: exploring update methods Best practices for smooth updates: a proactive approach Ubuntu Pro Troubleshooting update issues: when things don’t go as planned Summary Further reading
Getting Help: The Ubuntu Community and Beyond Chevron down icon Chevron up icon
Getting Help: The Ubuntu Community and Beyond Official Ubuntu documentation: a reliable reference The Ubuntu community: a wealth of knowledge and support Beyond Ubuntu: exploring online resources Reporting bugs Summary Further reading
Ubuntu in the Enterprise and at Scale Chevron down icon Chevron up icon
Ubuntu in the Enterprise and at Scale What is Landscape? Why use Landscape? Key features of Landscape Identity management Configuring authd at scale with Landscape Summary Further reading
Command-Line Tricks and Shortcuts: Boosting Your Efficiency Chevron down icon Chevron up icon
Command-Line Tricks and Shortcuts: Boosting Your Efficiency Mastering navigation: moving around with ease Essential file and directory management: taking control Advanced techniques: working with permissions and ownership Mastering text manipulation with powerful tools Process control Disk usage Hardware information Fun and useful utilities Beyond the basics: exploring advanced features Summary Further reading
Part 3: Security and Privacy Chevron down icon Chevron up icon
Part 3: Security and Privacy
Introduction to Network Security Chevron down icon Chevron up icon
Introduction to Network Security The connected world: a landscape of potential threats Building a secure foundation: essential security practices Summary Further reading
Understanding Firewalls Chevron down icon Chevron up icon
Understanding Firewalls Why you need a firewall? UFW: your friendly firewall Diving deeper with iptables Advanced routing with iptables Best practices Summary Further reading
Safeguarding Information with Data Encryption Chevron down icon Chevron up icon
Safeguarding Information with Data Encryption Why encrypt your entire disk? Securing your system with LUKS full disk encryption TPM-backed full disk encryption: enhanced security with hardware integration ZFS on root: a robust foundation with integrated encryption Encrypting your home directory Encrypting a USB drive Secure remote access with SSH Summary Further reading
Part 4: Ubuntu, the Ultimate Development Platform Chevron down icon Chevron up icon
Part 4: Ubuntu, the Ultimate Development Platform
Ubuntu for Developers Chevron down icon Chevron up icon
Ubuntu for Developers Why choose Ubuntu? Essential development tools on Ubuntu Beyond the basics: Advanced development resources Continuous learning and exploration Contributing to the open source community Summary
Leveraging Containers for Development Chevron down icon Chevron up icon
Leveraging Containers for Development The LXD advantage: Redefining development workflows LXD on Ubuntu: A step-by-step guide LXD container management Crafting development environments with LXD Creating and using your first LXD container Unlocking advanced LXD features for developers LXD in action: Real-world development use cases LXD versus Docker: Choosing the right containerization tool Best practices for LXD development The future of LXD in development Summary Further reading
Cloud-Style VMs on Your Desktop Chevron down icon Chevron up icon
Cloud-Style VMs on Your Desktop Why Multipass? Getting started with Multipass Multipass GUI Summary Further reading
Kubernetes Development on Your Desktop Chevron down icon Chevron up icon
Kubernetes Development on Your Desktop Why MicroK8s? Getting started with MicroK8s Developing with MicroK8s MicroK8s for specific workflows Advanced MicroK8s techniques Summary Further reading
Building Your Data Science Toolkit Chevron down icon Chevron up icon
Building Your Data Science Toolkit Why Canonical’s Data Science Stack? The components of Data Science Stack Getting started with Data Science Stack Leveraging Data Science Stack Advanced usage Summary Further reading
Embracing the Spirit of Ubuntu Chevron down icon Chevron up icon
Embracing the Spirit of Ubuntu A retrospective: exploring the potential of Ubuntu Beyond the book: the open road of exploration Conclusion: the spirit of Ubuntu
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C++ in Embedded Systems
C++ in Embedded Systems
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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++
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Part I: Introduction to C++ in Embedded Development Chevron down icon Chevron up icon
Part I: Introduction to C++ in Embedded Development
Debunking Common Myths about C++ Chevron down icon Chevron up icon
Debunking Common Myths about C++ Technical requirements A short history of C++ C with Classes Bloat and runtime overhead Summary
Challenges in Embedded Systems with Limited Resources Chevron down icon Chevron up icon
Challenges in Embedded Systems with Limited Resources Technical requirements Safety-critical and hard real-time embedded systems Dynamic memory management Disabling unwanted C++ features Summary
Embedded C++ Ecosystem Chevron down icon Chevron up icon
Embedded C++ Ecosystem Technical requirements Compilers and development environments Static analyzers Unit testing Profiling Summary
Setting Up the Development Environment for a C++ Embedded Project Chevron down icon Chevron up icon
Setting Up the Development Environment for a C++ Embedded Project Technical requirements Requirements for a modern software development environment Containerized development environment Containerized development environment and Visual Studio Code Summary
Part II: C++ Fundamentals Chevron down icon Chevron up icon
Part II: C++ Fundamentals
Classes – Building Blocks of C++ Applications Chevron down icon Chevron up icon
Classes – Building Blocks of C++ Applications Technical requirements Encapsulation Storage duration and initialization Inheritance and dynamic polymorphism Summary
Beyond Classes – Fundamental C++ Concepts Chevron down icon Chevron up icon
Beyond Classes – Fundamental C++ Concepts Technical requirements Namespaces Function overloading Interoperability with C References Standard library containers and algorithms Summary
Strengthening Firmware – Practical C++ Error Handling Methods Chevron down icon Chevron up icon
Strengthening Firmware – Practical C++ Error Handling Methods Technical requirements Error codes and asserts Exceptions std:: optional and std::expected Summary
Part III: C++ Advanced Concepts Chevron down icon Chevron up icon
Part III: C++ Advanced Concepts
Building Generic and Reusable Code with Templates Chevron down icon Chevron up icon
Building Generic and Reusable Code with Templates Technical requirements Template basics Template metaprogramming Concepts Compile-time polymorphism Summary
Improving Type-Safety with Strong Types Chevron down icon Chevron up icon
Improving Type-Safety with Strong Types Technical requirements Implicit conversion Explicit conversion Strong types Summary
Writing Expressive Code with Lambdas Chevron down icon Chevron up icon
Writing Expressive Code with Lambdas Technical requirements Lambda expression basics Storing lambdas using std::function std::function and dynamic memory allocation Summary
Compile-Time Computation Chevron down icon Chevron up icon
Compile-Time Computation Technical requirements Templates constexpr specifier consteval specifier Summary
Part IV: Applying C++ to Solving Embedded Domain Problems Chevron down icon Chevron up icon
Part IV: Applying C++ to Solving Embedded Domain Problems
Writing C++ HAL Chevron down icon Chevron up icon
Writing C++ HAL Technical requirements Memory-mapped peripherals Timers Summary
Working with C Libraries Chevron down icon Chevron up icon
Working with C Libraries Technical requirements Using C HAL in C++ projects Introducing static classes Using RAII for wrapping the littlefs C library Summary
Enhancing Super-Loop with Sequencer Chevron down icon Chevron up icon
Enhancing Super-Loop with Sequencer Technical requirements Super-loop and motivation for a sequencer Designing a sequencer Storing a callable Implementing a sequencer Summary
Practical Patterns – Building a Temperature Publisher Chevron down icon Chevron up icon
Practical Patterns – Building a Temperature Publisher Technical requirements The Observer pattern Runtime implementation Compile-time implementation Summary
Designing Scalable Finite State Machines Chevron down icon Chevron up icon
Designing Scalable Finite State Machines Technical requirements FSM – a simple implementation FSM – implementation using the State pattern State pattern implementation using tag dispatching Boost SML
Libraries and Frameworks Chevron down icon Chevron up icon
Libraries and Frameworks Technical requirements Standard library Embedded template library Pigweed Compile-time Initialization and Build Summary
Cross-Platform Development Chevron down icon Chevron up icon
Cross-Platform Development Technical requirements Importance of writing portable code SOLID design principles Testability Summary
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Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
By Sebastian Raschka
February 2022 | 774 pages
Icon Learn applied machine learning with a solid foundation in theory
Icon Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
Icon Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices
Giving Computers the Ability to Learn from Data Chevron down icon Chevron up icon
Giving Computers the Ability to Learn from Data Building intelligent machines to transform data into knowledge The three different types of machine learning Introduction to the basic terminology and notations A roadmap for building machine learning systems Using Python for machine learning Summary
Training Simple Machine Learning Algorithms for Classification Chevron down icon Chevron up icon
Training Simple Machine Learning Algorithms for Classification Artificial neurons – a brief glimpse into the early history of machine learning Implementing a perceptron learning algorithm in Python Adaptive linear neurons and the convergence of learning Summary
A Tour of Machine Learning Classifiers Using Scikit-Learn Chevron down icon Chevron up icon
A Tour of Machine Learning Classifiers Using Scikit-Learn Choosing a classification algorithm First steps with scikit-learn – training a perceptron Modeling class probabilities via logistic regression Maximum margin classification with support vector machines Solving nonlinear problems using a kernel SVM Decision tree learning K-nearest neighbors – a lazy learning algorithm Summary
Building Good Training Datasets – Data Preprocessing Chevron down icon Chevron up icon
Building Good Training Datasets – Data Preprocessing Dealing with missing data Handling categorical data Partitioning a dataset into separate training and test datasets Bringing features onto the same scale Selecting meaningful features Assessing feature importance with random forests Summary
Compressing Data via Dimensionality Reduction Chevron down icon Chevron up icon
Compressing Data via Dimensionality Reduction Unsupervised dimensionality reduction via principal component analysis Supervised data compression via linear discriminant analysis Nonlinear dimensionality reduction and visualization Summary
Learning Best Practices for Model Evaluation and Hyperparameter Tuning Chevron down icon Chevron up icon
Learning Best Practices for Model Evaluation and Hyperparameter Tuning Streamlining workflows with pipelines Using k-fold cross-validation to assess model performance Debugging algorithms with learning and validation curves Fine-tuning machine learning models via grid search Looking at different performance evaluation metrics Summary
Combining Different Models for Ensemble Learning Chevron down icon Chevron up icon
Combining Different Models for Ensemble Learning Learning with ensembles Combining classifiers via majority vote Bagging – building an ensemble of classifiers from bootstrap samples Leveraging weak learners via adaptive boosting Gradient boosting – training an ensemble based on loss gradients Summary
Applying Machine Learning to Sentiment Analysis Chevron down icon Chevron up icon
Applying Machine Learning to Sentiment Analysis Preparing the IMDb movie review data for text processing Introducing the bag-of-words model Training a logistic regression model for document classification Working with bigger data – online algorithms and out-of-core learning Topic modeling with latent Dirichlet allocation Summary
Predicting Continuous Target Variables with Regression Analysis Chevron down icon Chevron up icon
Predicting Continuous Target Variables with Regression Analysis Introducing linear regression Exploring the Ames Housing dataset Implementing an ordinary least squares linear regression model Fitting a robust regression model using RANSAC Evaluating the performance of linear regression models Using regularized methods for regression Turning a linear regression model into a curve – polynomial regression Dealing with nonlinear relationships using random forests Summary
Working with Unlabeled Data – Clustering Analysis Chevron down icon Chevron up icon
Working with Unlabeled Data – Clustering Analysis Grouping objects by similarity using k-means Organizing clusters as a hierarchical tree Locating regions of high density via DBSCAN Summary
Implementing a Multilayer Artificial Neural Network from Scratch Chevron down icon Chevron up icon
Implementing a Multilayer Artificial Neural Network from Scratch Modeling complex functions with artificial neural networks Classifying handwritten digits Training an artificial neural network About convergence in neural networks A few last words about the neural network implementation Summary
Parallelizing Neural Network Training with PyTorch Chevron down icon Chevron up icon
Parallelizing Neural Network Training with PyTorch PyTorch and training performance First steps with PyTorch Building input pipelines in PyTorch Building an NN model in PyTorch Choosing activation functions for multilayer neural networks Summary
Going Deeper – The Mechanics of PyTorch Chevron down icon Chevron up icon
Going Deeper – The Mechanics of PyTorch The key features of PyTorch PyTorch’s computation graphs PyTorch tensor objects for storing and updating model parameters Computing gradients via automatic differentiation Simplifying implementations of common architectures via the torch.nn module Project one – predicting the fuel efficiency of a car Project two – classifying MNIST handwritten digits Higher-level PyTorch APIs: a short introduction to PyTorch-Lightning Summary
Classifying Images with Deep Convolutional Neural Networks Chevron down icon Chevron up icon
Classifying Images with Deep Convolutional Neural Networks The building blocks of CNNs Putting everything together – implementing a CNN Implementing a deep CNN using PyTorch Smile classification from face images using a CNN Summary
Modeling Sequential Data Using Recurrent Neural Networks Chevron down icon Chevron up icon
Modeling Sequential Data Using Recurrent Neural Networks Introducing sequential data RNNs for modeling sequences Implementing RNNs for sequence modeling in PyTorch Summary
Transformers – Improving Natural Language Processing with Attention Mechanisms Chevron down icon Chevron up icon
Transformers – Improving Natural Language Processing with Attention Mechanisms Adding an attention mechanism to RNNs Introducing the self-attention mechanism Attention is all we need: introducing the original transformer architecture Building large-scale language models by leveraging unlabeled data Fine-tuning a BERT model in PyTorch Summary
Generative Adversarial Networks for Synthesizing New Data Chevron down icon Chevron up icon
Generative Adversarial Networks for Synthesizing New Data Introducing generative adversarial networks Implementing a GAN from scratch Improving the quality of synthesized images using a convolutional and Wasserstein GAN Other GAN applications Summary
Graph Neural Networks for Capturing Dependencies in Graph Structured Data Chevron down icon Chevron up icon
Graph Neural Networks for Capturing Dependencies in Graph Structured Data Introduction to graph data Understanding graph convolutions Implementing a GNN in PyTorch from scratch Implementing a GNN using the PyTorch Geometric library Other GNN layers and recent developments Summary
Reinforcement Learning for Decision Making in Complex Environments Chevron down icon Chevron up icon
Reinforcement Learning for Decision Making in Complex Environments Introduction – learning from experience The theoretical foundations of RL Reinforcement learning algorithms Implementing our first RL algorithm A glance at deep Q-learning Chapter and book summary
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Mastering Windows Server 2025
Mastering Windows Server 2025
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By Jordan Krause
October 2025 | 824 pages
Icon Configure, manage, secure, and troubleshoot Windows Server environments from the ground up
Icon Understand and take control of key components including AD, DNS, DHCP, GPO, and PKI
Icon Apply knowledge in real-world labs and walkthroughs that bring each server role to life
Icon Purchase of the print or Kindle book includes a free PDF eBook
Getting Started with Windows Server 2025 Chevron down icon Chevron up icon
Getting Started with Windows Server 2025 The purpose of Windows Server Your head in the clouds Windows Server versions and licensing Overview of new and updated features Features deprecated in Server 2025 Navigating the interface Windows Settings Task Manager Task View Summary Questions
Installation and Management Chevron down icon Chevron up icon
Installation and Management Technical requirements Installing Windows Server 2025 Installing roles and features Centralized management and monitoring Windows Admin Center Azure Arc Quick server rollouts with Sysprep In-place upgrading to Windows Server 2025 Summary Questions
Active Directory Chevron down icon Chevron up icon
Active Directory What is a domain controller? Creating your first domain Multiple domain controllers for redundancy Active Directory Users and Computers Active Directory Domains and Trusts Active Directory Sites and Services Active Directory Administrative Center Read-only domain controllers FSMO roles Demoting an old domain controller Intro to Group Policy Microsoft Entra ID Summary Questions
DNS and DHCP Chevron down icon Chevron up icon
DNS and DHCP The purpose of DNS Types of DNS records Split-brain DNS Types of DNS zones DNS-over-HTTPS IP addressing with DHCP Creating a DHCP scope DHCP reservations DHCP failover IPAM Summary Questions Join us on Discord
Group Policy Chevron down icon Chevron up icon
Group Policy Group Policy Objects Building a GPO Scoping a GPO Computer settings and user settings Policy versus preference Default Domain Policy Administrative Templates The Central Store Summary Questions
File Management Chevron down icon Chevron up icon
File Management Data is a company’s lifeblood File shares Automated drive mappings File permissions Distributed File System (DFS) File Transfer Protocol (FTP) SMB over QUIC Summary Questions Join us on Discord
Certificates Chevron down icon Chevron up icon
Certificates Common certificate types Planning your PKI Creating a certificate template Issuing certificates Obtaining a public authority SSL certificate Exporting and importing certificates OpenSSL for Linux webservers Summary Questions
Networking with Windows Server 2025 Chevron down icon Chevron up icon
Networking with Windows Server 2025 IPv4 “need-to-know” information Introduction to IPv6 Your networking toolbox The Windows routing table NIC teaming Software-defined networking Summary Questions Join us on Discord
Remote Access Chevron down icon Chevron up icon
Remote Access Regular ol’ VPN Always On VPN DirectAccess Remote Access Management Console DA, VPN, or AOVPN? Which is best? Web Application Proxy Summary Questions
Hardening and Security Chevron down icon Chevron up icon
Hardening and Security Microsoft Defender Antivirus Windows Defender Firewall: no laughing matter Encryption technologies Microsoft Entra Password Protection Fine-grained password policy Windows LAPS Advanced Threat Analytics: end of support General security best practices Summary Questions Join us on Discord
Server Core Chevron down icon Chevron up icon
Server Core Why use Server Core? Interfacing with Server Core Using Windows Admin Center to manage Server Core The SConfig utility Roles available in Server Core Building a Server Core domain controller What happened to Nano Server? Can we run Server Core in Azure? Summary Questions
PowerShell Chevron down icon Chevron up icon
PowerShell Why move to PowerShell? Working within PowerShell Importing a module Using a pipeline PowerShell Integrated Scripting Environment Remotely managing a server Desired State Configuration WinGet application management PowerShell for M365 Windows Terminal in Server 2022 Summary Questions
Redundancy in Windows Server 2025 Chevron down icon Chevron up icon
Redundancy in Windows Server 2025 Network Load Balancing Configuring a load-balanced website Failover clustering Clustering tiers Setting up a failover cluster Clustering improvements in Windows Server Storage Replica Storage Spaces Direct Summary Questions
Containers Chevron down icon Chevron up icon
Containers Understanding application containers Container base images Windows Server containers versus Hyper-V containers Docker and Kubernetes Working with containers Where is Azure in all this? Summary Questions Join us on Discord
Hyper-V Chevron down icon Chevron up icon
Hyper-V Designing and implementing Hyper-V server What’s new in 2025? Virtual switches Implementing a virtual server Managing a virtual server Shielded VMs Resilient Filesystem (ReFS) deduplication Integrating Hyper-V with Linux Hyper-V Server…2019? Summary Questions
Remote Desktop Services Chevron down icon Chevron up icon
Remote Desktop Services Wherefore art thou, role? Components of an RDS environment Publishing RDS sessions RDS licensing RDS user profiles RemoteApp RDS maintenance considerations Azure Virtual Desktop Summary Questions Join us on Discord
Troubleshooting Chevron down icon Chevron up icon
Troubleshooting Backup and restore Task Manager Resource Monitor Performance Monitor Sysinternals tools DTrace Windows Firewall with Advanced Security System Insights Remote toolsets Event Logs MMC and MSC shortcuts Feedback Hub Summary Questions
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Architectures for the Intelligent AI-Ready Enterprise
Architectures for the Intelligent AI-Ready Enterprise
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By Boris Bialek
September 2025 | 510 pages
Icon Complete guide covering GenAI to agentic AI, semantic protection to multi-agent systems
Icon 25+ proven AI use cases delivering measurable impact across 6+ industries
Icon 15+ real enterprise case studies from Novo Nordisk, Base 39, and more
Icon Purchase of the print or Kindle book includes a free PDF eBook
Part 1: AI and Key Concepts Chevron down icon Chevron up icon
Part 1: AI and Key Concepts
AI Modernization to Innovation Chevron down icon Chevron up icon
AI Modernization to Innovation Understanding innovation: Creating new value Understanding modernization: The often-overlooked prerequisite Modern data platforms: The backbone of AI-ready transformation Summary References
What Sets GenAI, RAG, and Agentic AI Apart Chevron down icon Chevron up icon
What Sets GenAI, RAG, and Agentic AI Apart How AI evolved: From theory to ChatGPT GenAI: Creating new content from patterns The embedding models and “embedders” RAG: Enhancing LLMs with contextual data Agentic AI: Automating decision-making and reasoning Summary References
The System of Action Chevron down icon Chevron up icon
The System of Action Building an AI-ready data foundation Practical considerations for AI data design Operationalizing a system of action database Summary References
Trustworthy AI, Compliance, and Data Governance Chevron down icon Chevron up icon
Trustworthy AI, Compliance, and Data Governance Why ethical AI matters Bridging principles and implementation Navigating the regulatory landscape Building trustworthy and responsible AI The road ahead: Emerging trends and future directions Summary References
Modernization Using AI Chevron down icon Chevron up icon
Modernization Using AI The modernization challenge Unlocking innovation with AI-powered modernization Summary References
Part 2: Real-World Case Studies and Implementations Chevron down icon Chevron up icon
Part 2: Real-World Case Studies and Implementations
Practical Applications of Agentic and GenAI in Manufacturing – Part 1 Chevron down icon Chevron up icon
Practical Applications of Agentic and GenAI in Manufacturing – Part 1 The path to success in manufacturing AI GenAI-powered supply chain optimization Atlas: Unified AI infrastructure Summary References
Practical Applications of Agentic and GenAI in Manufacturing – Part 2 Chevron down icon Chevron up icon
Practical Applications of Agentic and GenAI in Manufacturing – Part 2 Predictive maintenance and multi-agent collaboration Knowledge management and preservation Hyper-personalized in-cabin experiences Fleet management and optimization The expanding role of AI in manufacturing Summary References
AI-Driven Strategies for Media and Telecommunication Industries Chevron down icon Chevron up icon
AI-Driven Strategies for Media and Telecommunication Industries Evolving landscape of media and telecommunication Search generative experiences (SGEs) Agentic AIOps for network management The expanding role of AI in media and telecommunication Summary References
Cognigy’s Voice and Chatbots in the Time of Agentic AI Chevron down icon Chevron up icon
Cognigy’s Voice and Chatbots in the Time of Agentic AI The evolution from rule-based to goal-oriented AI Why data is the lifeblood of agentic AI Real-time performance in critical moments Scaling excellence, not mistakes Personalization isn’t magic, it’s data mastery Summary References
Harnessing AI to Transform the Retail Industry Chevron down icon Chevron up icon
Harnessing AI to Transform the Retail Industry Semantic search powered by vector search Personalized marketing and content generation Demand forecasting and predictive analytics Digitizing in-store interactions with intelligence Conversational and agentic chatbots The expanding role of AI in retail Summary References
Financial Services and the Next Wave of AI Chevron down icon Chevron up icon
Financial Services and the Next Wave of AI The evolution of AI in finance Revolutionizing enterprise knowledge management in banking with GenAI Better digital banking experiences through AI‑driven interactions AI-enhanced financial crime mitigation and compliance Multimodal and AI-driven ESG analysis Straight-through payments processing powered by AI Capital markets Summary References
RegData, MongoDB, and Voyage AI: Semantic Data Protection in FSI Chevron down icon Chevron up icon
RegData, MongoDB, and Voyage AI: Semantic Data Protection in FSI The data protection dilemma in financial AI Understanding the MongoDB, RegData, and Voyage AI approach to semantic data protection Building the future with advanced techniques and emerging standards Summary References
Driving Client Success in Banking with GenAI Copilots Chevron down icon Chevron up icon
Driving Client Success in Banking with GenAI Copilots The catch-22 of wealth relationship management GenAI factory: Powering copilots, agents, and GenAI apps What now? How enterprises can succeed with GenAI Summary
Delivering Business Value with AI in Insurance Chevron down icon Chevron up icon
Delivering Business Value with AI in Insurance The evolution of data architectures Architecture features for agentic systems AI-driven improvements in claim handling for better business outcomes Practical AI use cases in insurance The future of AI in insurance Summary References
Automating Insurance Underwriting with Fireworks AI and MongoDB Chevron down icon Chevron up icon
Automating Insurance Underwriting with Fireworks AI and MongoDB Understanding the importance of speed Setting up the core technical components Diving deep into the technical innovation Summary References
AI-Powered Transformation of Healthcare and Life Sciences Chevron down icon Chevron up icon
AI-Powered Transformation of Healthcare and Life Sciences Understanding the AI revolution in healthcare The transformative opportunity of GenAI Preparing for the healthcare data needs of the future Implementation architecture patterns Extending intelligence to life sciences Summary References
Part 3: The Future of Intelligent Enterprise Chevron down icon Chevron up icon
Part 3: The Future of Intelligent Enterprise
Enterprise Document Management with MongoDB and AI Chevron down icon Chevron up icon
Enterprise Document Management with MongoDB and AI The digital filing cabinet era Redefining document management with Encore Summary References
Democratizing Agentic AI for Enterprise with Dataworkz and MongoDB Chevron down icon Chevron up icon
Democratizing Agentic AI for Enterprise with Dataworkz and MongoDB Tailoring AI for every organization Implementing effective AI solutions with Dataworkz and MongoDB Summary
Outlook: Beyond Today’s AI Chevron down icon Chevron up icon
Outlook: Beyond Today’s AI From tools to context: The rise of intelligent architectures Looking back to look forward: Patterns in the field Foundational architecture: From theory to practice Final thought: Architecture is the intelligence References
Afterword Chevron down icon Chevron up icon
Afterword What we learned Architectural insights Beyond case studies What’s next
Index Chevron down icon Chevron up icon
Index
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Responsive Web Design with HTML5 and CSS
Responsive Web Design with HTML5 and CSS
Read now
By Ben Frain
October 2025 | 576 pages
Icon Understand what responsive web design is, and learn to design effective, accessible, and future-proof front-end applications
Icon Explore the newest CSS features like view transitions, scroll-driven animations, P3 colors, and CSS functions
Icon Learn brand-new time-saving additions to the HTML language, including dialogs and modals
The Fundamentals of Responsive Web Design Chevron down icon Chevron up icon
The Fundamentals of Responsive Web Design
The Essentials of Responsive Web Design Chevron down icon Chevron up icon
The Essentials of Responsive Web Design The browser and device landscape Defining responsive web design Should you be coding with AI? Our first responsive example Summary
Writing HTML Markup Chevron down icon Chevron up icon
Writing HTML Markup Getting the start of HTML pages right All hail the mighty <a> element New semantic elements in HTML5 HTML text-level semantics Obsolete HTML features Putting HTML elements to use WCAG accessibility conformance and WAI-ARIA for more accessible web applications Embedding media in HTML5 Providing alternate media sources Audio and video tags work almost identically The loading attribute The <dialog> element Summary An exercise
Media and Container Queries Chevron down icon Chevron up icon
Media and Container Queries The meta viewport tag Why media queries are needed for a responsive web design Media query syntax Using media queries to alter a design Advanced media query considerations Media Queries Level 5 Container queries Summary
Fluid Layout and Flexbox Chevron down icon Chevron up icon
Fluid Layout and Flexbox Converting a fixed pixel design into a fluid proportional layout Introducing flexbox Getting flexy inline-flex Flexbox alignment properties Visually re-ordering items Wrapping with flex Summary
Layout with CSS Grid Chevron down icon Chevron up icon
Layout with CSS Grid What CSS Grid is and the problems it solves Setting up a Grid Layout Placing and sizing Grid Layout items Placing items in the grid Shorthand syntax Allowing nested elements to take part in the grid The subgrid value Summary Applying what you have learned so far
Core Skills for Effective Frontend Web Development Chevron down icon Chevron up icon
Core Skills for Effective Frontend Web Development
CSS Nesting, Layers, Selectors, and More Chevron down icon Chevron up icon
CSS Nesting, Layers, Selectors, and More CSS nesting Cascade layers Selectors, units, and capabilities CSS selectors – beyond the normal! CSS structural pseudo-classes Combinator selectors – child, next sibling, and subsequent sibling Grouping selectors Limiting the application of selectors with @scope Responsive viewport relative lengths Using @supports to fork CSS Summary
Web Typography Chevron down icon Chevron up icon
Web Typography System fonts The @font-face CSS rule Optimizing font loading with font-display Better text alignment with text-box Controlling the flow of text with text-wrap Variable fonts Summary Exercise
CSS Colors Chevron down icon Chevron up icon
CSS Colors RGB color HSL color Space-separated functional color notation Color concepts and terminology The current state of CSS color Fallbacks for older browsers The color-mix() function The contrast-color() function Relative colors with the relative color syntax Summary Resources
Stunning Aesthetics with CSS Chevron down icon Chevron up icon
Stunning Aesthetics with CSS Text shadows Box shadows Background gradients Multiple background images CSS filters A warning on CSS performance CSS clip-path mask-image mix-blend-mode Summary
Responsive Images Chevron down icon Chevron up icon
Responsive Images Modern image formats AVIF image format WebP image format Image format comparisons Using the picture element to progressively enhance images The inherent problem of responsive images Simple resolution switching with srcset A practical note on providing alternate image formats and sizes Summary
SVG Chevron down icon Chevron up icon
SVG A brief history of SVG An image that is also a readable web document Creating SVGs with popular image-editing packages and services Inserting SVGs into your web pages Reusing graphical objects from symbols Recoloring SVG assets with a mask What you can do with each SVG insertion method (inline, object, background-image, and img) Extra SVG capabilities and oddities Optimizing SVGs SVG implementation tips Summary Further resources
Transitions, Transformations, and Animations Chevron down icon Chevron up icon
Transitions, Transformations, and Animations What CSS transitions are and how we can use them CSS 2D transforms CSS 3D transformations Animating with CSS Using @starting-style to transition items entering the DOM Animating height with the calc-size() function Summary Exercises and training
Custom Properties and CSS Functions Chevron down icon Chevron up icon
Custom Properties and CSS Functions Working with custom properties CSS functions Putting it all together Summary
Forms Chevron down icon Chevron up icon
Forms HTML5 forms HTML5 input types Styling HTML5 forms with CSS Summary
Latest Platform Features and Parting Advice Chevron down icon Chevron up icon
Latest Platform Features and Parting Advice
Cutting-Edge CSS Features Chevron down icon Chevron up icon
Cutting-Edge CSS Features The Popover API Anchor positioning Scroll-driven animations View transitions Summary
Bonus Techniques and Parting Advice Chevron down icon Chevron up icon
Bonus Techniques and Parting Advice Truncating text Creating horizontal scrolling panels Parting advice Summary
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