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LLM Engineer's Handbook
LLM Engineer's Handbook
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
By Paul Iusztin
October 2024 | 522 pages
Icon Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning
Icon Learn essential skills for deploying and monitoring LLMs, ensuring optimal performance in production
Icon Utilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applications
Understanding the LLM Twin Concept and Architecture Chevron down icon Chevron up icon
Understanding the LLM Twin Concept and Architecture Understanding the LLM Twin concept Planning the MVP of the LLM Twin product Building ML systems with feature/training/inference pipelines Designing the system architecture of the LLM Twin Summary References
Tooling and Installation Chevron down icon Chevron up icon
Tooling and Installation Python ecosystem and project installation MLOps and LLMOps tooling Databases for storing unstructured and vector data Preparing for AWS Summary References
Data Engineering Chevron down icon Chevron up icon
Data Engineering Designing the LLM Twin’s data collection pipeline Gathering raw data into the data warehouse Summary References
RAG Feature Pipeline Chevron down icon Chevron up icon
RAG Feature Pipeline Understanding RAG An overview of advanced RAG Exploring the LLM Twin’s RAG feature pipeline architecture Implementing the LLM Twin’s RAG feature pipeline Summary References
Supervised Fine-Tuning Chevron down icon Chevron up icon
Supervised Fine-Tuning Creating an instruction dataset Creating our own instruction dataset Exploring SFT and its techniques Fine-tuning in practice Summary References
Fine-Tuning with Preference Alignment Chevron down icon Chevron up icon
Fine-Tuning with Preference Alignment Understanding preference datasets Creating our own preference dataset Preference alignment Implementing DPO Summary References
Evaluating LLMs Chevron down icon Chevron up icon
Evaluating LLMs Model evaluation RAG evaluation Evaluating TwinLlama-3.1-8B Summary References
Inference Optimization Chevron down icon Chevron up icon
Inference Optimization Model optimization strategies Model parallelism Model quantization Summary References
RAG Inference Pipeline Chevron down icon Chevron up icon
RAG Inference Pipeline Understanding the LLM Twin’s RAG inference pipeline Exploring the LLM Twin’s advanced RAG techniques Implementing the LLM Twin’s RAG inference pipeline Summary References
Inference Pipeline Deployment Chevron down icon Chevron up icon
Inference Pipeline Deployment Criteria for choosing deployment types Understanding inference deployment types Monolithic versus microservices architecture in model serving Exploring the LLM Twin’s inference pipeline deployment strategy Deploying the LLM Twin service Autoscaling capabilities to handle spikes in usage Summary References
MLOps and LLMOps Chevron down icon Chevron up icon
MLOps and LLMOps The path to LLMOps: Understanding its roots in DevOps and MLOps Deploying the LLM Twin’s pipelines to the cloud Adding LLMOps to the LLM Twin Summary References
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
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Index
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Generative AI with Python and PyTorch
Generative AI with Python and PyTorch
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By Joseph Babcock
March 2025 | 450 pages
Icon Implement real-world applications of LLMs and generative AI
Icon Fine-tune models with PEFT and LoRA to speed up training
Icon Expand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndex
Icon Purchase of the print or Kindle book includes a free eBook in PDF format
Introduction to Generative AI: Drawing Data from Models Chevron down icon Chevron up icon
Introduction to Generative AI: Drawing Data from Models Discriminative versus generative models Implementing generative models The rules of probability Discriminative and generative modeling, and Bayes’ theorem Why generative models? Unique challenges of generative models Summary References
Building Blocks of Deep Neural Networks Chevron down icon Chevron up icon
Building Blocks of Deep Neural Networks Perceptrons: A brain in a function Multilayer perceptrons and backpropagation Varieties of networks: convolution and recursive Networks for sequential data Transformers Building a better optimizer Summary References
The Rise of Methods for Text Generation Chevron down icon Chevron up icon
The Rise of Methods for Text Generation Text representation Text generation and the magic of LSTMs LSTM variants and convolutions for text Summary References
NLP 2.0: Using Transformers to Generate Text Chevron down icon Chevron up icon
NLP 2.0: Using Transformers to Generate Text Attention Self-attention Transformers NLP tasks and transformer architectures DistilBERT in action Text generation with GPT Summary References Join our communities on Discord and Reddit
LLM Foundations Chevron down icon Chevron up icon
LLM Foundations Recap: Transformer architectures Updated training setup Instruction fine-tuning Hands-on: Instruction tuning Reinforcement Learning with Human Feedback (RLHF) Hands-on: RLHF using PPO LLMs Summary
Open-Source LLMs Chevron down icon Chevron up icon
Open-Source LLMs The LLaMA models Mixtral Dolly Falcon Grok-1 Summary References Join our communities on Discord and Reddit
Prompt Engineering Chevron down icon Chevron up icon
Prompt Engineering Prompt engineering Prompting techniques Cross-domain prompting Adversarial prompting Limitations of prompt engineering Summary References
LLM Toolbox Chevron down icon Chevron up icon
LLM Toolbox The LangChain ecosystem Building a simple LLM application Creating complex applications with LangGraph Summary References Join our communities on Discord and Reddit
LLM Optimization Techniques Chevron down icon Chevron up icon
LLM Optimization Techniques Why optimize? Pre-training optimizations Fine-tuning optimizations Inference time improvements Emerging trends and research areas Summary References
Emerging Applications in Generative AI Chevron down icon Chevron up icon
Emerging Applications in Generative AI Advances in model development New usages for LLMs Summary References
Neural Networks Using VAEs Chevron down icon Chevron up icon
Neural Networks Using VAEs Creating separable encodings of images The variational objective The reparameterization trick Inverse autoregressive flow Importing CIFAR Creating the network in PyTorch Summary References Join our communities on Discord and Reddit
Image Generation with GANs Chevron down icon Chevron up icon
Image Generation with GANs Generative adversarial networks Vanilla GAN Improved GANs Challenges Summary References Join our communities on Discord and Reddit
Style Transfer with GANs Chevron down icon Chevron up icon
Style Transfer with GANs Pix2Pix-GAN: paired style transfer CycleGAN: unpaired style transfer Summary References Join our communities on Discord and Reddit
Deepfakes with GANs Chevron down icon Chevron up icon
Deepfakes with GANs Deepfakes overview Modes of operation Key feature set High-level workflow Re-enactment using Pix2Pix Challenges Off-the-shelf implementations Summary References Join our communities on Discord and Reddit
Diffusion Models and AI Art Chevron down icon Chevron up icon
Diffusion Models and AI Art A walk through image generation: Why we need diffusion models Running Stable Diffusion in the cloud Deep dive into the text-to-image pipeline Summary References Join our communities on Discord and Reddit
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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Full-Stack AI and Mastering AI Agents Bootcamp
Full-Stack AI and Mastering AI Agents Bootcamp
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By Vivian Aranha
April 2025 | 307 pages
Icon A comprehensive approach to AI development, covering text processing, NLP, and AI agent creation
Icon Expertise in industry-standard tools like Ollama, LLaMA, and Mistral, essential for building scalable AI systems
Icon A structured learning path that covers environment setup, Python coding, and real-world model deployment
Full Stack AI: Quick Start - Setting Up Ollama & First AI Model Chevron down icon Chevron up icon
Introduction to Section: Quick Start - Setting Up Ollama & First AI Model Introduction to Ollama Installing & Setting Up Ollama Up and Running with Python Your First AI Project: AI Chat Assistant
Full Stack AI: AI for Text Processing (NLP & LLMs) Chevron down icon Chevron up icon
Introduction to Section AI for Text Processing (NLP & LLMs) AI-Powered Text Summarizer using Mistral AI Model AI Blog & Content Writer using LLaMA 3 AI Model AI Code Generator & Debugger using CodeLlama AI Model Grammar & Spell Checker using Deepseek R1 AI Model AI Legal Document Analyzer using Phi-2 AI Model Real-Time AI News Summarizer using Qwen 2.5 AI Model
Full Stack AI: AI for Conversational Bots & Assistants Chevron down icon Chevron up icon
Introduction to Section AI for Conversational Bots & Assistants Customer Support Chatbot - QWQ AI Model AI-Powered Virtual Assistant - LLaMA 2 Medical AI Symptom Checker - MedLLaMA 2 E-Commerce AI Product Recommender - Granite 3.2
AI Agents Bootcamp: Introduction to AI Agents & Tools Chevron down icon Chevron up icon
Goals for Day 1: Introduction to AI Agents & Tools What are AI Agents? Why Use Ollama for AI Agents? Setting Up Ollama Download a Model for AI Agents Python Basics for AI (Optional) Build a Simple AI Agent Adding Memory to the AI Agent Building a Web UI for the AI Agent
AI Agents Bootcamp: Building a Personal AI Assistant Chevron down icon Chevron up icon
Goals for Day 2: Building a Personal AI Assistant Install Dependencies Build the AI Voice Assistant How the AI Assistant Works Running the AI Assistant Create a Web-Based AI Voice Assistant
AI Agents Bootcamp: AI-Powered Web Scraper Agent Chevron down icon Chevron up icon
Goals for Day 3: AI-Powered Web Scraper Agent Install Dependencies Build the AI Web Scraper How the AI Web Scraper Works Running the AI Web Scraper Store Scraped Data in a Vector Database
AI Agents Bootcamp: AI-Powered Document Reader & Q&A Bot Chevron down icon Chevron up icon
Goals for Day 4: AI-Powered Document Reader & Q&A Bot Install Dependencies Build the AI Document Reader How the AI Document Reader Works Running the AI Document Reader Enable File Download of AI-Summarized Reports
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C# 13 and .NET 9 – Modern Cross-Platform Development Fundamentals
C# 13 and .NET 9 – Modern Cross-Platform Development Fundamentals
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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
Hello, C#! Welcome, .NET! Introducing this book and its contents Setting up your development environment Understanding .NET Building console apps using Visual Studio Building console apps using VS Code Making good use of the GitHub repository for this book Looking for help Practicing and exploring Summary
Speaking C# Chevron down icon Chevron up icon
Speaking C# Introducing the C# language Discovering your C# compiler version Understanding C# grammar and vocabulary Working with variables Exploring more about console apps Practicing and exploring Summary
Controlling Flow, Converting Types, and Handling Exceptions Chevron down icon Chevron up icon
Controlling Flow, Converting Types, and Handling Exceptions Operating on variables Understanding selection statements Understanding iteration statements Storing multiple values in an array Casting and converting between types Handling exceptions Checking for overflow Practicing and exploring Summary
Writing, Debugging, and Testing Functions Chevron down icon Chevron up icon
Writing, Debugging, and Testing Functions Writing functions Debugging during development Hot reloading during development Unit testing Throwing and catching exceptions in functions Practicing and exploring Summary
Building Your Own Types with Object-Oriented Programming Chevron down icon Chevron up icon
Building Your Own Types with Object-Oriented Programming Talking about OOP Building class libraries Storing data in fields Working with methods and tuples Controlling access with properties and indexers Pattern matching with objects Working with record types Practicing and exploring Summary
Implementing Interfaces and Inheriting Classes Chevron down icon Chevron up icon
Implementing Interfaces and Inheriting Classes Setting up a class library and console application Static methods and overloading operators Making types safely reusable with generics Raising and handling events Implementing interfaces Working with null values Inheriting from classes Casting within inheritance hierarchies Inheriting and extending .NET types Summarizing custom type choices Practicing and exploring Summary
Packaging and Distributing .NET Types Chevron down icon Chevron up icon
Packaging and Distributing .NET Types The road to .NET 9 Understanding .NET components Publishing your code for deployment Native ahead-of-time compilation Packaging your libraries for NuGet distribution Working with preview features Practicing and exploring Summary
Working with Common .NET Types Chevron down icon Chevron up icon
Working with Common .NET Types Working with numbers Working with text Pattern matching with regular expressions Storing multiple objects in collections Working with spans, indexes, and ranges Practicing and exploring Summary
Working with Files, Streams, and Serialization Chevron down icon Chevron up icon
Working with Files, Streams, and Serialization Managing a filesystem Reading and writing with streams Encoding and decoding text Serializing object graphs Practicing and exploring Summary
Working with Data Using Entity Framework Core Chevron down icon Chevron up icon
Working with Data Using Entity Framework Core Understanding modern databases Setting up EF Core in a .NET project Defining EF Core models Querying EF Core models Practicing and exploring Summary
Querying and Manipulating Data Using LINQ Chevron down icon Chevron up icon
Querying and Manipulating Data Using LINQ Writing LINQ expressions LINQ in practice Sorting and more Using LINQ with EF Core Joining, grouping, and lookups Practicing and exploring Summary
Introducing Modern Web Development Using .NET Chevron down icon Chevron up icon
Introducing Modern Web Development Using .NET Understanding ASP.NET Core Structuring projects Building an entity model for use in the rest of the book Understanding web development Practicing and exploring Summary
Building Websites Using ASP.NET Core Chevron down icon Chevron up icon
Building Websites Using ASP.NET Core Exploring ASP.NET Core History of Blazor Exploring Blazor static SSR Using Entity Framework Core with ASP.NET Core Practicing and exploring Summary
Building Interactive Web Components Using Blazor Chevron down icon Chevron up icon
Building Interactive Web Components Using Blazor Reviewing the Blazor Web App project template Building components using Blazor Defining forms using the EditForm component Practicing and exploring Summary
Building and Consuming Web Services Chevron down icon Chevron up icon
Building and Consuming Web Services Building web services using ASP.NET Core Creating a web service for the Northwind database Documenting and trying out web services Consuming web services using HTTP clients Practicing and exploring Summary
Epilogue Chevron down icon Chevron up icon
Epilogue Next steps on your C# and .NET learning journey The tenth edition for .NET 10 Good luck! Leave a Review!
Index Chevron down icon Chevron up icon
Index
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C++ Memory Management
C++ Memory Management
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Full star icon Full star icon Full star icon Full star icon Full star icon 5
By Patrice Roy
March 2025 | 434 pages
Icon Use contemporary C++ features to enhance code safety and reliability
Icon Develop a deeper understanding of memory management mechanisms in C++
Icon Write programs that adapt to the constraints of your chosen architecture or platform
Icon Purchase of the print or Kindle book includes a free PDF eBook
Part 1: Memory in C++ Chevron down icon Chevron up icon
Part 1: Memory in C++
Chapter 1: Objects, Pointers, and References Chevron down icon Chevron up icon
Chapter 1: Objects, Pointers, and References Technical requirements Representation of memory in C++ Understanding the fundamental properties of objects Arrays Summary
Chapter 2: Things to Be Careful With Chevron down icon Chevron up icon
Chapter 2: Things to Be Careful With Different kinds of evil Pointers Type punning Summary
Chapter 3: Casts and cv-qualifications Chevron down icon Chevron up icon
Chapter 3: Casts and cv-qualifications Technical requirements What is a cast? Safety in the type system – cv-qualifications The C++ casts Summary
Part 2: Implicit Memory Management Techniques Chevron down icon Chevron up icon
Part 2: Implicit Memory Management Techniques
Chapter 4: Using Destructors Chevron down icon Chevron up icon
Chapter 4: Using Destructors Technical requirements On destructors: a short recap Managing resources The RAII idiom Some pitfalls Standard resource management automation tools Summary
Chapter 5: Using Standard Smart Pointers Chevron down icon Chevron up icon
Chapter 5: Using Standard Smart Pointers Technical requirements The standard smart pointers Type unique_ptr Types shared_ptr and weak_ptr When to use raw pointers Summary
Chapter 6: Writing Smart Pointers Chevron down icon Chevron up icon
Chapter 6: Writing Smart Pointers Technical requirements Ownership semantics Writing your own (naïve) unique_ptr Writing your own (naïve) shared_ptr Writing a policy-based duplicating pointer Some not-so-smart yet useful smart pointers Summary
Part 3: Taking Control (of Memory Management Mechanisms) Chevron down icon Chevron up icon
Part 3: Taking Control (of Memory Management Mechanisms)
Chapter 7: Overloading Memory Allocation Operators Chevron down icon Chevron up icon
Chapter 7: Overloading Memory Allocation Operators Why would one overload allocation functions? Brief overview of the C language allocation functions Overview of the C++ allocation operators Summary
Chapter 8: Writing a Naïve Leak Detector Chevron down icon Chevron up icon
Chapter 8: Writing a Naïve Leak Detector Technical requirements The plan A first implementation (that almost works) Identifying (and fixing) the problems Revisiting our implementation (and lessons learned) Summary
Chapter 9: Atypical Allocation Mechanisms Chevron down icon Chevron up icon
Chapter 9: Atypical Allocation Mechanisms Technical requirements Placement new and memory-mapped hardware Simplifying nothrow new usage Out-of-memory situations and new_handler Standard C++ and exotic memory Summary
Chapter 10: Arena-Based Memory Management and Other Optimizations Chevron down icon Chevron up icon
Chapter 10: Arena-Based Memory Management and Other Optimizations Technical requirements Arena-based memory management When parameters change Chunked pools Summary
Chapter 11: Deferred Reclamation Chevron down icon Chevron up icon
Chapter 11: Deferred Reclamation Technical requirements What do we mean by deferred reclamation? Reclamation (without finalization) at the end of the program Reclamation and finalization at the end of the program Reclamation and finalization at the end of the scope Summary
Part 4: Writing Generic Containers (and a Bit More) Chevron down icon Chevron up icon
Part 4: Writing Generic Containers (and a Bit More)
Chapter 12: Writing Generic Containers with Explicit Memory Management Chevron down icon Chevron up icon
Chapter 12: Writing Generic Containers with Explicit Memory Management Technical requirements Writing your own vector<T> alternative Writing your own forward_list<T> alternative Better memory management Summary
Chapter 13: Writing Generic Containers with Implicit Memory Management Chevron down icon Chevron up icon
Chapter 13: Writing Generic Containers with Implicit Memory Management Technical requirements Why explicit memory management complicates our implementation Implicit memory management with a smart pointer Consequences of this redesign Generalizing to ForwardList<T>? Summary
Chapter 14: Writing Generic Containers with Allocator Support Chevron down icon Chevron up icon
Chapter 14: Writing Generic Containers with Allocator Support Technical requirements Why allocators? Traditional allocators Polymorphic memory resource allocators Summary
Chapter 15: Contemporary Issues Chevron down icon Chevron up icon
Chapter 15: Contemporary Issues Technical requirements Starting object lifetime without constructors Trivial relocation Type-aware allocation and deallocation functions Summary
Annexure: Things You Should Know Chevron down icon Chevron up icon
Annexure: Things You Should Know struct and class std::size_t The sizeof operator Assertions Undefined behavior Type traits The std::true_type and std::false_type traits The std::conditional<B,T,F> trait Algorithms Functors (function objects) and lambdas Friends The decltype operator Perfect forwarding The singleton design pattern The std::exchange() function
Index Chevron down icon Chevron up icon
Index Why subscribe?
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Building AI Agents - From Fundamentals to Advanced Applications
Building AI Agents - From Fundamentals to Advanced Applications
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By Clarian North
March 2025 | 165 pages
Icon In-depth coverage of AI agents from basic concepts to advanced applications
Icon Hands-on experience with N8N for building and managing automation pipelines
Icon Focus on security, ethical considerations, and real-world use cases of AI agents
The AI Agent Personal Assistant Demonstration Chevron down icon Chevron up icon
Demo Of Our AI Agent Review Of The Course Fundamentals
What Are AI Agents For Beginners Chevron down icon Chevron up icon
The AI Agent Presentation Overview Difference between LLMs and AI Agents AI Agents Are Autonomous Inherently How AI Agents Are Goal Oriented And Adaptive Intelligence And Efficiency Of AI Agents Personalizing Experiences with AI Agents And Potential Real World Examples Of AI Agents What Are The Types Of Different AI Agents Risks and Concerns with AI Agents Security And Ethical Considerations with AI Agents What Are The Upcoming Trends With AI Agents The Rise Of Intelligent Automation Is Here
What Is N8N And How To Use It Chevron down icon Chevron up icon
What Is N8N How To Get Started With N8N Working With N8N And Getting Familiar How Does N8N Work With AI Agents
Setting Up An AI Agent and Credentials Chevron down icon Chevron up icon
What Are AI Agent Triggers Set Up Your First AI Agent Trigger Event What Is The Data Flow System Process In N8N Basic Fundamentals On How To View JSON Data
How To Build A Text Classifier For AI Agents Chevron down icon Chevron up icon
Setting Up A Text Classifier For AI Agents Create Labels In Gmail For Our Classifier Why Use A Text Classifier Instead Of An LLM OOP Under The Hood In N8N Without Doing Any Coding Prompt Designing The Text Classifier Model For Our AI Agent Generating An OpenAI API Key Customize Test Runs For Text Classifier Pipelines
Multi Level Pipelines For AI Agents Chevron down icon Chevron up icon
Build out Label App Event Pipelines For Multi Level AI Agent Actions Build A Personal Assistant Agent For Email Responses Supervised Learning Prompt Model For AI Agent Optimizing AI Personal Assistant Agent With Consistent Data Flow
Building An AI Agent For Scheduling Your Meetings Chevron down icon Chevron up icon
Data Flow For Pipelines with AI Agents Review Setting Up Google Calendar App Action For Our Agent System Prompt Engineering And Tweaking with Trial and Error System Prompting With AI Message Models For Content Processing Customizing Nodes For Function Codes To Automate AI Workflow in N8N Review End To End Workflow Automation And Final Conclusion
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Building Agentic AI Systems
Building Agentic AI Systems
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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
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 Join our communities on Discord and Reddit
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 Join our communities on Discord and Reddit
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 Join our communities on Discord and Reddit
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 Join our communities on Discord and Reddit
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 Join our communities on Discord and Reddit
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 Join our communities on Discord and Reddit
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 Join our communities on Discord and Reddit
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 Join our communities on Discord and Reddit
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 Join our communities on Discord and Reddit
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 Join our communities on Discord and Reddit
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 Join our communities on Discord and Reddit
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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Practical Generative AI with ChatGPT
Practical Generative AI with ChatGPT
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Full star icon Full star icon Full star icon Full star icon Empty star icon 4
By Valentina Alto
April 2025 | 386 pages
Icon Turn ChatGPT into your companion for marketing, research, personal productivity, art and coding
Icon Learn prompt engineering techniques that deliver consistent, relevant, and ethical AI-powered results
Icon Build custom GPTs and assistants tailored to your specific business needs and workflows
Icon Purchase of the print or Kindle book includes a free PDF eBook
Fundamentals of Generative AI and OpenAI Chevron down icon Chevron up icon
Fundamentals of Generative AI and OpenAI
Introduction to Generative AI Chevron down icon Chevron up icon
Introduction to Generative AI Introducing generative AI Domains of generative AI Main trends and innovations Legal and ethical landscape of generative AI Summary References
OpenAI and ChatGPT: Beyond the Market Hype Chevron down icon Chevron up icon
OpenAI and ChatGPT: Beyond the Market Hype Technical requirements What is OpenAI? An overview of OpenAI model families Getting started with ChatGPT Summary References
ChatGPT in Action Chevron down icon Chevron up icon
ChatGPT in Action
Understanding Prompt Engineering Chevron down icon Chevron up icon
Understanding Prompt Engineering Technical requirements What is prompt engineering? Understanding zero-, one-, and few-shot learning Principles of prompt engineering Exploring some advanced techniques Ethical considerations to avoid bias Summary References
Boosting Day-to-Day Productivity with ChatGPT Chevron down icon Chevron up icon
Boosting Day-to-Day Productivity with ChatGPT Technical requirements ChatGPT as a daily assistant Generating text Improving writing skills and translation Quick information retrieval and competitive intelligence Summary
Developing the Future with ChatGPT Chevron down icon Chevron up icon
Developing the Future with ChatGPT Technical requirements Why should developers use ChatGPT? Generating, optimizing, and debugging code Generating documentation and code explainability Understanding ML model interpretability Translation among different programming languages Working with code on canvas Summary
Mastering Marketing with ChatGPT Chevron down icon Chevron up icon
Mastering Marketing with ChatGPT Technical requirements Leveraging ChatGPT for marketing New product development and the go-to-market strategy A/B testing for marketing comparison Boosting SEO Sentiment analysis for quality and customer satisfaction Summary
Research Reinvented with ChatGPT Chevron down icon Chevron up icon
Research Reinvented with ChatGPT Researchers’ need for ChatGPT Brainstorming literature for your study Providing support for the design and framework of your experiment Generating and formatting a bibliography Generating a presentation of the study Summary References
Unleashing Creativity Visually with ChatGPT Chevron down icon Chevron up icon
Unleashing Creativity Visually with ChatGPT What is multimodality? Prompt design to generate stunning illustrations with DALL-E Leveraging ChatGPT as a designer assistant Exploring advanced plugins within the GPT store Summary References
Exploring GPTs Chevron down icon Chevron up icon
Exploring GPTs Technical requirements What are GPTs? Personal assistant Code assistant Marketing assistant Research assistant Summary References
OpenAI for Enterprises Chevron down icon Chevron up icon
OpenAI for Enterprises
Leveraging OpenAI’s Models for Enterprise-Scale Applications Chevron down icon Chevron up icon
Leveraging OpenAI’s Models for Enterprise-Scale Applications Technical requirements How GenAI is disrupting industries Understanding OpenAI models’ APIs Architectural patterns to build applications with models’ APIs Introducing the public cloud: Azure OpenAI Summary References
Epilogue and Final Thoughts Chevron down icon Chevron up icon
Epilogue and Final Thoughts An overview of what we have learned so far It’s not all about OpenAI Ethical implications of generative AI and why we need responsible AI What to expect in the near future Summary References
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
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Index
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Python Machine Learning By Example
Python Machine Learning By Example
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
By Yuxi (Hayden) Liu
July 2024 | 518 pages
Icon Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling
Icon Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions
Icon Implement ML models, such as neural networks and linear and logistic regression, from scratch
Icon Purchase of the print or Kindle book includes a free PDF copy
Getting Started with Machine Learning and Python Chevron down icon Chevron up icon
Getting Started with Machine Learning and Python An introduction to machine learning Knowing the prerequisites Getting started with three types of machine learning Digging into the core of machine learning Data preprocessing and feature engineering Combining models Installing software and setting up Summary Exercises Join our book’s Discord space
Building a Movie Recommendation Engine with Naïve Bayes Chevron down icon Chevron up icon
Building a Movie Recommendation Engine with Naïve Bayes Getting started with classification Exploring Naïve Bayes Implementing Naïve Bayes Building a movie recommender with Naïve Bayes Evaluating classification performance Tuning models with cross-validation Summary Exercises References Join our book’s Discord space
Predicting Online Ad Click-Through with Tree-Based Algorithms Chevron down icon Chevron up icon
Predicting Online Ad Click-Through with Tree-Based Algorithms A brief overview of ad click-through prediction Getting started with two types of data – numerical and categorical Exploring a decision tree from the root to the leaves Implementing a decision tree from scratch Implementing a decision tree with scikit-learn Predicting ad click-through with a decision tree Ensembling decision trees – random forests Ensembling decision trees – gradient-boosted trees Summary Exercises Join our book’s Discord space
Predicting Online Ad Click-Through with Logistic Regression Chevron down icon Chevron up icon
Predicting Online Ad Click-Through with Logistic Regression Converting categorical features to numerical – one-hot encoding and ordinal encoding Classifying data with logistic regression Training a logistic regression model Training on large datasets with online learning Handling multiclass classification Implementing logistic regression using TensorFlow Summary Exercises Join our book’s Discord space
Predicting Stock Prices with Regression Algorithms Chevron down icon Chevron up icon
Predicting Stock Prices with Regression Algorithms What is regression? Mining stock price data Getting started with feature engineering Estimating with linear regression Estimating with decision tree regression Implementing a regression forest Evaluating regression performance Predicting stock prices with the three regression algorithms Summary Exercises Join our book’s Discord space
Predicting Stock Prices with Artificial Neural Networks Chevron down icon Chevron up icon
Predicting Stock Prices with Artificial Neural Networks Demystifying neural networks Building neural networks Picking the right activation functions Preventing overfitting in neural networks Predicting stock prices with neural networks Summary Exercises Join our book’s Discord space
Mining the 20 Newsgroups Dataset with Text Analysis Techniques Chevron down icon Chevron up icon
Mining the 20 Newsgroups Dataset with Text Analysis Techniques How computers understand language – NLP Touring popular NLP libraries and picking up NLP basics Getting the newsgroups data Exploring the newsgroups data Thinking about features for text data Visualizing the newsgroups data with t-SNE Summary Exercises Join our book’s Discord space
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling Chevron down icon Chevron up icon
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling Learning without guidance – unsupervised learning Getting started with k-means clustering Clustering newsgroups dataset Discovering underlying topics in newsgroups Summary Exercises Join our book’s Discord space
Recognizing Faces with Support Vector Machine Chevron down icon Chevron up icon
Recognizing Faces with Support Vector Machine Finding the separating boundary with SVM Classifying face images with SVM Estimating with support vector regression Summary Exercises Join our book’s Discord space
Machine Learning Best Practices Chevron down icon Chevron up icon
Machine Learning Best Practices Machine learning solution workflow Best practices in the data preparation stage Best practices in the training set generation stage Best practices in the model training, evaluation, and selection stage Best practices in the deployment and monitoring stage Summary Exercises Join our book’s Discord space
Categorizing Images of Clothing with Convolutional Neural Networks Chevron down icon Chevron up icon
Categorizing Images of Clothing with Convolutional Neural Networks Getting started with CNN building blocks Architecting a CNN for classification Exploring the clothing image dataset Classifying clothing images with CNNs Boosting the CNN classifier with data augmentation Improving the clothing image classifier with data augmentation Advancing the CNN classifier with transfer learning Summary Exercises Join our book’s Discord space
Making Predictions with Sequences Using Recurrent Neural Networks Chevron down icon Chevron up icon
Making Predictions with Sequences Using Recurrent Neural Networks Introducing sequential learning Learning the RNN architecture by example Training an RNN model Overcoming long-term dependencies with LSTM Analyzing movie review sentiment with RNNs Revisiting stock price forecasting with LSTM Writing your own War and Peace with RNNs Summary Exercises Join our book’s Discord space
Advancing Language Understanding and Generation with the Transformer Models Chevron down icon Chevron up icon
Advancing Language Understanding and Generation with the Transformer Models Understanding self-attention Exploring the Transformer’s architecture Improving sentiment analysis with BERT and Transformers Generating text using GPT Summary Exercises Join our book’s Discord space
Building an Image Search Engine Using CLIP: a Multimodal Approach Chevron down icon Chevron up icon
Building an Image Search Engine Using CLIP: a Multimodal Approach Introducing the CLIP model Getting started with the dataset Finding images with words Summary Exercises References Join our book’s Discord space
Making Decisions in Complex Environments with Reinforcement Learning Chevron down icon Chevron up icon
Making Decisions in Complex Environments with Reinforcement Learning Setting up the working environment Introducing OpenAI Gym and Gymnasium Introducing reinforcement learning with examples Solving the FrozenLake environment with dynamic programming Performing Monte Carlo learning Solving the Blackjack problem with the Q-learning algorithm Summary Exercises Join our book’s Discord space
Other Books You May Enjoy Chevron down icon Chevron up icon
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React Key Concepts
React Key Concepts
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
By Maximilian Schwarzmüller
January 2025 | 544 pages
Icon Work through clear, concise explanations of core React 19 functionalities
Icon Complete practical exercises that challenge you to build your own simple apps
Icon Discover fullstack React with Next.js, React Server Components, Suspense, and more
React – What and Why Chevron down icon Chevron up icon
React – What and Why Introduction What is React? The Problem with “Vanilla JavaScript” React and Declarative Code Introducing SPAs Summary and Key Takeaways Join Us on Discord
Understanding React Components and JSX Chevron down icon Chevron up icon
Understanding React Components and JSX Introduction What Are Components? What Does React Do with All These Components? JSX vs HTML vs Vanilla JavaScript Moving Beyond Static Content When Should You Split Components? Summary and Key Takeaways Apply What You Learned
Components and Props Chevron down icon Chevron up icon
Components and Props Introduction Can Components Do More? Using Props in Components Components, Props, and Reusability Summary and Key Takeaways Apply What You Learned
Working with Events and State Chevron down icon Chevron up icon
Working with Events and State Introduction What’s the Problem? Updating State Correctly Working with Multiple State Values Deriving Values from State Summary and Key Takeaways Apply What You Learned
Rendering Lists and Conditional Content Chevron down icon Chevron up icon
Rendering Lists and Conditional Content Introduction What Are Conditional Content and List Data? Rendering Content Conditionally Outputting List Data Summary and Key Takeaways Apply What You Learned
Styling React Apps Chevron down icon Chevron up icon
Styling React Apps Introduction How Does Styling Work in React Apps? The Problem with Unscoped Styles Summary and Key Takeaways Apply What You Learned
Portals and Refs Chevron down icon Chevron up icon
Portals and Refs Introduction A World without Refs Refs versus State Using Refs for More than DOM Access React and Where Things End up in the DOM Summary and Key Takeaways Apply What You Have Learned
Handling Side Effects Chevron down icon Chevron up icon
Handling Side Effects Introduction What’s the Problem? Understanding Side Effects Dealing with Side Effects with the useEffect() Hook Effects and Their Dependencies Summary and Key Takeaways Apply What You Learned
Handling User Input & Forms with Form Actions Chevron down icon Chevron up icon
Handling User Input & Forms with Form Actions Introduction Handling Form Submissions without Actions Handling Form Submissions with Actions Behind the Scenes: Actions Are Transitions Managing State Based on Form Submissions Performing Optimistic Updates Summary and Key Takeaways Apply What You Learned
Behind the Scenes of React and Optimization Opportunities Chevron down icon Chevron up icon
Behind the Scenes of React and Optimization Opportunities Introduction Revisiting Component Evaluations and Updates The Virtual DOM vs the Real DOM Avoiding Unnecessary Code Download Strict Mode Debugging Code and the React Developer Tools Summary and Key Takeaways Apply What You Learned
Working with Complex State Chevron down icon Chevron up icon
Working with Complex State Introduction A Problem with Cross-Component State Using Context to Handle Multi-Component State Using the Context API Efficiently Limitations of useState( ) Managing State with useReducer( ) Summary and Key Takeaways Apply What You Learned
Building Custom React Hooks Chevron down icon Chevron up icon
Building Custom React Hooks Introduction Introducing Custom Hooks Custom Hooks: A Flexible Feature A More Complex Example Using Custom Hooks for Context Access Summary and Key Takeaways Apply What You Learned
Multipage Apps with React Router Chevron down icon Chevron up icon
Multipage Apps with React Router Introduction One Page Is Not Enough Getting Started with React Router and Defining Routes From Static to Dynamic Routes Redirecting Summary and Key Takeaways Apply What You Learned
Managing Data with React Router Chevron down icon Chevron up icon
Managing Data with React Router Introduction Data Fetching and Routing Are Tightly Coupled Loading Data with React Router Layouts Revisited Handling Errors Onward to Data Submission Summary and Key Takeaways Apply What You Learned
Server-side Rendering & Building Fullstack Apps with Next.js Chevron down icon Chevron up icon
Server-side Rendering & Building Fullstack Apps with Next.js Introduction What’s the Problem with Client-Side React Apps? Making Sense of Server-side Rendering (SSR) Adding SSR to a React Application Introducing Next.js Diving Deeper into Next.js Summary and Key Takeaways Apply What You Learned
React Server Components & Server Actions Chevron down icon Chevron up icon
React Server Components & Server Actions Introduction The Problem with Server-side Data Fetching Introducing RSCs From Data Fetching to Data Mutations Summary and Key Takeaways Apply What You Learned
Understanding React Suspense & The use() Hook Chevron down icon Chevron up icon
Understanding React Suspense & The use() Hook Introduction Showing Granular Fallback Content with Suspense Suspense Usage Patterns Should You Fetch Data via Suspense or useEffect()? Summary and Key Takeaways Test Your Knowledge! Apply What You Learned
Next Steps and Further Resources Chevron down icon Chevron up icon
Next Steps and Further Resources Introduction How Should You Proceed? Final Words Join Us on Discord Why Subscribe?
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy Share Your Thoughts
Index Chevron down icon Chevron up icon
Index Download the Free PDF and Supplementary Content Description of Supplementary Content
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Learn Python Programming
Learn Python Programming
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Full star icon Full star icon Full star icon Full star icon Full star icon 5
By Romano
November 2024 | 616 pages
Icon Create and deploy APIs and CLI applications, leveraging Python’s strengths in scripting and automation
Icon Stay current with the latest features and improvements in Python, including pattern matching and the latest exception handling syntax
Icon Engage with new real-world examples and projects, including competitive programming problems, to solidify your understanding of Python
A Gentle Introduction to Python Chevron down icon Chevron up icon
A Gentle Introduction to Python A brief introduction to programming Enter the Python About Python What are the drawbacks? Who is using Python today? Setting up the environment How to run a Python program How is Python code organized? Python’s execution model Guidelines for writing good code Python culture A note on IDEs A word about AI Summary
Built-In Data Types Chevron down icon Chevron up icon
Built-In Data Types Everything is an object Mutability Numbers Immutable sequences Mutable sequences Set types Mapping types: dictionaries Data types Final considerations Summary
Conditionals and Iteration Chevron down icon Chevron up icon
Conditionals and Iteration Conditional programming Looping Assignment expressions Putting all this together A quick peek at the itertools module Summary
Functions, the Building Blocks of Code Chevron down icon Chevron up icon
Functions, the Building Blocks of Code Why use functions? Scopes and name resolution Input parameters Return values A few useful tips Recursive functions Anonymous functions Function attributes Built-in functions Documenting your code Importing objects One final example Summary
Comprehensions and Generators Chevron down icon Chevron up icon
Comprehensions and Generators The map, zip, and filter functions Comprehensions Generators Some performance considerations Do not overdo comprehensions and generators Name localization Generation behavior in built-ins One last example Summary
OOP, Decorators, and Iterators Chevron down icon Chevron up icon
OOP, Decorators, and Iterators Decorators OOP Writing a custom iterator Summary
Exceptions and Context Managers Chevron down icon Chevron up icon
Exceptions and Context Managers Exceptions Context managers Summary
Files and Data Persistence Chevron down icon Chevron up icon
Files and Data Persistence Working with files and directories Data interchange formats I/O, streams, and requests Persisting data on disk Configuration files Summary
Cryptography and Tokens Chevron down icon Chevron up icon
Cryptography and Tokens The need for cryptography Hashlib HMAC Secrets JSON Web Tokens Useful references Summary
Testing Chevron down icon Chevron up icon
Testing Testing your application Test-driven development Summary
Debugging and Profiling Chevron down icon Chevron up icon
Debugging and Profiling Debugging techniques Troubleshooting guidelines Profiling Python Summary
Introduction to Type Hinting Chevron down icon Chevron up icon
Introduction to Type Hinting Python approach to types History of type hinting Benefits of type hinting Type annotations The Mypy static type checker Some useful resources Summary
Data Science in Brief Chevron down icon Chevron up icon
Data Science in Brief IPython and Jupyter Notebook Dealing with data Where do we go from here? Summary
Introduction to API Development Chevron down icon Chevron up icon
Introduction to API Development The Hypertext Transfer Protocol APIs – An introduction The railway API Where do we go from here? Summary
CLI Applications Chevron down icon Chevron up icon
CLI Applications Command-line arguments Building a CLI client for the railway API Other resources and tools Summary
Packaging Python Applications Chevron down icon Chevron up icon
Packaging Python Applications The Python Package Index Packaging with Setuptools Building and publishing packages Advice for starting new projects Other files Alternative tools Further reading Summary
Programming Challenges Chevron down icon Chevron up icon
Programming Challenges Advent of Code Final considerations Other programming challenge websites Summary
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
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Index
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Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
By Sebastian Raschka
February 2022 | 774 pages
Icon Learn applied machine learning with a solid foundation in theory
Icon Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
Icon Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices
Giving Computers the Ability to Learn from Data Chevron down icon Chevron up icon
Giving Computers the Ability to Learn from Data Building intelligent machines to transform data into knowledge The three different types of machine learning Introduction to the basic terminology and notations A roadmap for building machine learning systems Using Python for machine learning Summary
Training Simple Machine Learning Algorithms for Classification Chevron down icon Chevron up icon
Training Simple Machine Learning Algorithms for Classification Artificial neurons – a brief glimpse into the early history of machine learning Implementing a perceptron learning algorithm in Python Adaptive linear neurons and the convergence of learning Summary
A Tour of Machine Learning Classifiers Using Scikit-Learn Chevron down icon Chevron up icon
A Tour of Machine Learning Classifiers Using Scikit-Learn Choosing a classification algorithm First steps with scikit-learn – training a perceptron Modeling class probabilities via logistic regression Maximum margin classification with support vector machines Solving nonlinear problems using a kernel SVM Decision tree learning K-nearest neighbors – a lazy learning algorithm Summary
Building Good Training Datasets – Data Preprocessing Chevron down icon Chevron up icon
Building Good Training Datasets – Data Preprocessing Dealing with missing data Handling categorical data Partitioning a dataset into separate training and test datasets Bringing features onto the same scale Selecting meaningful features Assessing feature importance with random forests Summary
Compressing Data via Dimensionality Reduction Chevron down icon Chevron up icon
Compressing Data via Dimensionality Reduction Unsupervised dimensionality reduction via principal component analysis Supervised data compression via linear discriminant analysis Nonlinear dimensionality reduction and visualization Summary
Learning Best Practices for Model Evaluation and Hyperparameter Tuning Chevron down icon Chevron up icon
Learning Best Practices for Model Evaluation and Hyperparameter Tuning Streamlining workflows with pipelines Using k-fold cross-validation to assess model performance Debugging algorithms with learning and validation curves Fine-tuning machine learning models via grid search Looking at different performance evaluation metrics Summary
Combining Different Models for Ensemble Learning Chevron down icon Chevron up icon
Combining Different Models for Ensemble Learning Learning with ensembles Combining classifiers via majority vote Bagging – building an ensemble of classifiers from bootstrap samples Leveraging weak learners via adaptive boosting Gradient boosting – training an ensemble based on loss gradients Summary
Applying Machine Learning to Sentiment Analysis Chevron down icon Chevron up icon
Applying Machine Learning to Sentiment Analysis Preparing the IMDb movie review data for text processing Introducing the bag-of-words model Training a logistic regression model for document classification Working with bigger data – online algorithms and out-of-core learning Topic modeling with latent Dirichlet allocation Summary
Predicting Continuous Target Variables with Regression Analysis Chevron down icon Chevron up icon
Predicting Continuous Target Variables with Regression Analysis Introducing linear regression Exploring the Ames Housing dataset Implementing an ordinary least squares linear regression model Fitting a robust regression model using RANSAC Evaluating the performance of linear regression models Using regularized methods for regression Turning a linear regression model into a curve – polynomial regression Dealing with nonlinear relationships using random forests Summary
Working with Unlabeled Data – Clustering Analysis Chevron down icon Chevron up icon
Working with Unlabeled Data – Clustering Analysis Grouping objects by similarity using k-means Organizing clusters as a hierarchical tree Locating regions of high density via DBSCAN Summary
Implementing a Multilayer Artificial Neural Network from Scratch Chevron down icon Chevron up icon
Implementing a Multilayer Artificial Neural Network from Scratch Modeling complex functions with artificial neural networks Classifying handwritten digits Training an artificial neural network About convergence in neural networks A few last words about the neural network implementation Summary
Parallelizing Neural Network Training with PyTorch Chevron down icon Chevron up icon
Parallelizing Neural Network Training with PyTorch PyTorch and training performance First steps with PyTorch Building input pipelines in PyTorch Building an NN model in PyTorch Choosing activation functions for multilayer neural networks Summary
Going Deeper – The Mechanics of PyTorch Chevron down icon Chevron up icon
Going Deeper – The Mechanics of PyTorch The key features of PyTorch PyTorch’s computation graphs PyTorch tensor objects for storing and updating model parameters Computing gradients via automatic differentiation Simplifying implementations of common architectures via the torch.nn module Project one – predicting the fuel efficiency of a car Project two – classifying MNIST handwritten digits Higher-level PyTorch APIs: a short introduction to PyTorch-Lightning Summary
Classifying Images with Deep Convolutional Neural Networks Chevron down icon Chevron up icon
Classifying Images with Deep Convolutional Neural Networks The building blocks of CNNs Putting everything together – implementing a CNN Implementing a deep CNN using PyTorch Smile classification from face images using a CNN Summary
Modeling Sequential Data Using Recurrent Neural Networks Chevron down icon Chevron up icon
Modeling Sequential Data Using Recurrent Neural Networks Introducing sequential data RNNs for modeling sequences Implementing RNNs for sequence modeling in PyTorch Summary
Transformers – Improving Natural Language Processing with Attention Mechanisms Chevron down icon Chevron up icon
Transformers – Improving Natural Language Processing with Attention Mechanisms Adding an attention mechanism to RNNs Introducing the self-attention mechanism Attention is all we need: introducing the original transformer architecture Building large-scale language models by leveraging unlabeled data Fine-tuning a BERT model in PyTorch Summary
Generative Adversarial Networks for Synthesizing New Data Chevron down icon Chevron up icon
Generative Adversarial Networks for Synthesizing New Data Introducing generative adversarial networks Implementing a GAN from scratch Improving the quality of synthesized images using a convolutional and Wasserstein GAN Other GAN applications Summary
Graph Neural Networks for Capturing Dependencies in Graph Structured Data Chevron down icon Chevron up icon
Graph Neural Networks for Capturing Dependencies in Graph Structured Data Introduction to graph data Understanding graph convolutions Implementing a GNN in PyTorch from scratch Implementing a GNN using the PyTorch Geometric library Other GNN layers and recent developments Summary
Reinforcement Learning for Decision Making in Complex Environments Chevron down icon Chevron up icon
Reinforcement Learning for Decision Making in Complex Environments Introduction – learning from experience The theoretical foundations of RL Reinforcement learning algorithms Implementing our first RL algorithm A glance at deep Q-learning Chapter and book summary
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
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Solutions Architect's Handbook
Solutions Architect's Handbook
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
By Saurabh Shrivastava
March 2024 | 578 pages
Icon Hits all the key areas -Rajesh Sheth, VP, Elastic Block Store, AWS
Icon Offers the knowledge you need to succeed in the evolving landscape of tech architecture - Luis Lopez Soria, Senior Specialist Solutions Architect, Google
Icon A valuable resource for enterprise strategists looking to build resilient applications - Cher Simon, Principal Solutions Architect, AWS
Solutions Architects in Organizations Chevron down icon Chevron up icon
Solutions Architects in Organizations What is solution architecture? The solutions architect’s role Understanding a solutions architect’s responsibilities Solutions architect in an Agile organization Common challenges in the solutions architect role Career path and skill development for solutions architects Summary
Principles of Solution Architecture Design Chevron down icon Chevron up icon
Principles of Solution Architecture Design Building scalable architecture design Building a highly available and resilient architecture Making your architecture fault-tolerant Designing for performance Creating immutable architecture Think loose coupling Think service, not server Think data-driven design Adding security everywhere Making applications usable and accessible Building future-proof extendable and reusable architecture Ensuring architectural interoperability and portability Applying automation everywhere Plan for business continuity Design for operation Overcoming architectural constraints Summary
Cloud Migration and Cloud Architecture Design Chevron down icon Chevron up icon
Cloud Migration and Cloud Architecture Design Public, private, and hybrid clouds Solution architecture in the public cloud Creating a cloud migration strategy Choosing a cloud migration strategy Steps for cloud migration Creating a hybrid cloud architecture Taking a multi-cloud approach Implementing CloudOps CloudOps pillars Summary Further reading
Solution Architecture Design Patterns Chevron down icon Chevron up icon
Solution Architecture Design Patterns Building an n-tier layered architecture Creating a multi-tenant SaaS-based architecture Understanding service-oriented architecture Building a cache-based architecture Model-View-Controller (MVC) architecture Building Domain-Driven Design (DDD) Understanding the circuit breaker pattern Implementing the bulkhead pattern Creating a floating IP pattern Deploying an application with a container Database handling in application architecture Clean Architecture Avoiding anti-patterns in solution architecture Summary
Cloud-Native Architecture Design Patterns Chevron down icon Chevron up icon
Cloud-Native Architecture Design Patterns What is cloud-native architecture? Building serverless architecture Building stateless and stateful architectural designs Creating a microservice architecture Reactive architecture Building queue-based architecture Pipes-and-Filters Architecture Creating Event-Driven Architecture Backend for Frontend pattern Cloud-native architecture anti-patterns Summary
Performance Considerations Chevron down icon Chevron up icon
Performance Considerations Design principles for high-performance architecture Technology selection for performance optimization Performance considerations for mobile applications Performance testing Managing performance monitoring Summary
Security Considerations Chevron down icon Chevron up icon
Security Considerations Design principles for architectural security Selecting technology for architectural security Security and compliance certifications The cloud’s shared security responsibility model Securing threat modeling Summary
Architectural Reliability Considerations Chevron down icon Chevron up icon
Architectural Reliability Considerations Design principles for architectural reliability Technology selection for architectural reliability Improving reliability with the cloud Summary
Operational Excellence Considerations Chevron down icon Chevron up icon
Operational Excellence Considerations Design principles for operational excellence Selecting technologies for operational excellence Achieving operational excellence in the public cloud Driving efficiency with CloudOps Summary
Cost Considerations Chevron down icon Chevron up icon
Cost Considerations Design principles for cost optimization Understanding techniques for cost optimization Driving cost optimization in the public cloud Green IT and its influence on cost considerations Summary
DevOps and Solution Architecture Framework Chevron down icon Chevron up icon
DevOps and Solution Architecture Framework Introducing DevOps Understanding the components of DevOps Introducing DevSecOps for Security Combining DevSecOps and CI/CD Implementing a CD strategy Best practices for choosing the right deployment strategy Implementing continuous testing in the CI/CD pipeline Using DevOps tools for CI/CD Implementing DevOps best practices Building DevOps and DevSecOps in the cloud Summary
Data Engineering for Solution Architecture Chevron down icon Chevron up icon
Data Engineering for Solution Architecture What is big data architecture? Designing big data processing pipelines Data ingestion, storage, processing, and analytics Data storage in the cloud Visualizing data Designing big data architectures Big data architecture best practices Summary
Machine Learning Architecture Chevron down icon Chevron up icon
Machine Learning Architecture What is machine learning? Working with data science and machine learning Machine learning in the cloud Building machine learning architecture Design principles for machine learning architecture MLOps Deep learning NLP Summary
Generative AI Architecture Chevron down icon Chevron up icon
Generative AI Architecture What is generative AI? Generative AI use cases The basic architecture of generative AI systems Popular generative AI FMs How to start with generative AI Generative AI reference architecture for building a mortgage assistant app Challenges in implementing generative AI Summary
Rearchitecting Legacy Systems Chevron down icon Chevron up icon
Rearchitecting Legacy Systems Learning the challenges of legacy systems Defining a strategy for system modernization Looking at legacy system modernization techniques Defining a cloud migration strategy for legacy systems Mainframe migration with the public cloud Modernizing legacy code with generative AI Summary
Solution Architecture Document Chevron down icon Chevron up icon
Solution Architecture Document Purpose of the SAD Views of the SAD Structure of the SAD Life cycle of the SAD SAD best practices and common pitfalls IT procurement documentation for a solution architecture Summary
Learning Soft Skills to Become a Better Solutions Architect Chevron down icon Chevron up icon
Learning Soft Skills to Become a Better Solutions Architect Importance of soft skills in solution architecture Acquiring pre-sales skills Taking ownership and accountability Being flexible and adaptable Design thinking Being a builder by engaging in coding hands-on Becoming better with continuous learning Being a mentor to others Becoming a technology evangelist and thought leader Summary
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
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Build Anything With AI Agents - Apps - Games - Assistants
Build Anything With AI Agents - Apps - Games - Assistants
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By Clarian North
March 2025 | 230 pages
Icon Cutting-edge AI technologies like OpenAI, DeepSeek, and Ollama are utilized throughout the course
Icon AI-driven applications and games are built using a practical, hands-on approach
Icon Deep dive into the training, quantization, and optimization of LLMs
What Actually is AI and LLMs - The Race Between OpenAI and DeepSeek Chevron down icon Chevron up icon
Welcome To The AI Revolution Artificial General Intelligence - Surpassing Human Intelligence Ethical Considerations of OpenAI Vs The Commodification Of DeepSeek What Are LLMs And Natural Language Processing How Are LLMs Trained And Their Implications What Is Reinforcement Learning And Why It Matters MoE Architecture And DeepSeek's Biggest Secret Difference Between RL And Pure RL With OpenAI And DeepSeek CPU Vs GPU And Parallel Processing With DeepSeek The Future Trends Of AI And AI Agents
Asking LLMs Like DeepSeek To Solve Problems For You Chevron down icon Chevron up icon
Prompt Engineering Introduction And DeepSeek Inference Build Your Very First Game From Scratch With DeepSeek What Is Ollama And Why Is It An AI Game Changer
Building Applications With Your Own Private AI Locally On Your Computer Chevron down icon Chevron up icon
What Are Parameters In Machine Learning And Model Variations With DeepSeek Running LLMs Offline On Your Computer Directly Commodifying LLMs Through Quantization Build A Resume Website With DeepSeek AI Locally
Command A Team Of AI Agents To Build You Applications Chevron down icon Chevron up icon
Heads Up On The Following Section Video What Is Github And Node And Setting Up Bolt.diy Build A Fullstack Application With AI Agents
Build Personal Assistant AI Agents Chevron down icon Chevron up icon
What AI Agents Can Do For Businesses What Is Make.com - Set Up An Account For Free How To Set Up An Automation Trigger For AI Agents Build An AI Agent For Chain Emails
How To Have AI Agents Write Code For You In Your Text Editor Chevron down icon Chevron up icon
Setting Up VS Code Install Cline Using AI Agents To Write Programs For You Build A Full Game Locally With AI Agents
Build AI Agent Researchers To Browse The Internet Chevron down icon Chevron up icon
Setting Up Playwright And Browser Access For AI Running AI Agents To Browse The Internet For You
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Learning Angular
Learning Angular
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Full star icon Full star icon Full star icon Full star icon Empty star icon 4
By Aristeidis Bampakos
January 2025 | 486 pages
Icon Explore the basics of Angular 19 development, from components and templates to forms, routing, and services
Icon Get up to speed with the new paradigms modern Angular brings, including standalone APIs, new control flow syntax, Signals, and server-side rendering (SSR)
Icon Discover best practices for building, deploying, debugging, and testing Angular applications
Building Your First Angular Application Chevron down icon Chevron up icon
Building Your First Angular Application Technical requirements What is Angular? Why choose Angular? Setting up the Angular CLI workspace The structure of an Angular application Angular tooling Summary Join us on Discord
Introduction to TypeScript Chevron down icon Chevron up icon
Introduction to TypeScript Technical requirements JavaScript essentials What is TypeScript? Getting started with TypeScript
Structuring User Interfaces with Components Chevron down icon Chevron up icon
Structuring User Interfaces with Components Technical requirements Creating our first component Interacting with the template Component inter-communication Encapsulating CSS styling Deciding on a change detection strategy Introducing the component lifecycle Summary
Enriching Applications Using Pipes and Directives Chevron down icon Chevron up icon
Enriching Applications Using Pipes and Directives Technical requirements Manipulating data with pipes Building pipes Building directives Summary
Managing Complex Tasks with Services Chevron down icon Chevron up icon
Managing Complex Tasks with Services Technical requirements Introducing Angular DI Creating our first Angular service Providing dependencies across the application Injecting services in the component tree Overriding providers in the injector hierarchy Summary
Reactive Patterns in Angular Chevron down icon Chevron up icon
Reactive Patterns in Angular Technical requirements Strategies for handling asynchronous information Reactive programming in Angular The RxJS library Subscribing to observables Unsubscribing from observables Summary
Tracking Application State with Signals Chevron down icon Chevron up icon
Tracking Application State with Signals Technical requirements Understanding signals Reading and writing signals Computed signals Cooperating with RxJS Summary
Communicating with Data Services over HTTP Chevron down icon Chevron up icon
Communicating with Data Services over HTTP Technical requirements Communicating data over HTTP Introducing the Angular HTTP client Setting up a backend API Handling CRUD data in Angular Authentication and authorization with HTTP Summary
Navigating through Applications with Routing Chevron down icon Chevron up icon
Navigating through Applications with Routing Technical requirements Introducing the Angular router Configuring the main routes Organizing application routes Passing parameters to routes Enhancing navigation with advanced features Summary
Collecting User Data with Forms Chevron down icon Chevron up icon
Collecting User Data with Forms Technical requirements Introducing web forms Building template-driven forms Building reactive forms Validating input in forms Manipulating form state Summary
Handling Application Errors Chevron down icon Chevron up icon
Handling Application Errors Technical requirements Handling runtime errors Demystifying framework errors Summary
Introduction to Angular Material Chevron down icon Chevron up icon
Introduction to Angular Material Technical requirements Introducing Material Design Introducing Angular Material Integrating UI components Summary
Unit Testing Angular Applications Chevron down icon Chevron up icon
Unit Testing Angular Applications Technical requirements Why do we need unit tests? The anatomy of a unit test Introducing unit tests in Angular Testing components Testing services Testing pipes Testing directives Testing forms Testing the router Summary
Bringing Applications to Production Chevron down icon Chevron up icon
Bringing Applications to Production Technical requirements Building an Angular application Limiting the application bundle size Optimizing the application bundle Deploying an Angular application Summary
Optimizing Application Performance Chevron down icon Chevron up icon
Optimizing Application Performance Technical requirements Introducing Core Web Vitals Rendering SSR applications Optimizing image loading Deferring components Prerendering SSG applications Summary
Index Chevron down icon Chevron up icon
Index
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The Chief AI Officer's Handbook
The Chief AI Officer's Handbook
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
By Jarrod Anderson
January 2025 | 336 pages
Icon Explore practical frameworks for effective AI team building and team management
Icon Implement and Optimize AI Agents and Agentic Systems through design and implementation of AI agents.
Icon Gain actionable advice for leveraging AI to drive innovation and strategic growth
Icon Purchase of the print or Kindle book includes a free PDF eBook
Part 1: The Role and Responsibilities of the Chief AI Officer Chevron down icon Chevron up icon
Part 1: The Role and Responsibilities of the Chief AI Officer
Chapter 1: Why Every Company Needs a Chief AI Officer Chevron down icon Chevron up icon
Chapter 1: Why Every Company Needs a Chief AI Officer The strategic necessity for a CAIO The strategic importance of AI leadership Alignment of AI initiatives with business goals Reflection and practical next steps Summary Questions References
Chapter 2: Key Responsibilities of a Chief AI Officer Chevron down icon Chevron up icon
Chapter 2: Key Responsibilities of a Chief AI Officer The problem – pain points and challenges The solution – step-by-step implementation Case study – transforming operations at APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Chapter 3: Crafting a Winning AI Strategy Chevron down icon Chevron up icon
Chapter 3: Crafting a Winning AI Strategy The problem – pain points and challenges The solution – a step-by-step implementation Hypothetical case study – transforming operations at APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Chapter 4: Building High-Performing AI Teams Chevron down icon Chevron up icon
Chapter 4: Building High-Performing AI Teams The problem – pain points and challenges Solution and process for building exceptional AI teams Step-by-step implementation for building a high-performing AI team Hypothetical case study – transforming APEX’s manufacturing and distribution with AI Reflection and practical next steps Summary Questions References
Part 2: Building and Implementing AI Systems Chevron down icon Chevron up icon
Part 2: Building and Implementing AI Systems
Chapter 5: Data – the Lifeblood of AI Chevron down icon Chevron up icon
Chapter 5: Data – the Lifeblood of AI The problem – pain points and challenges The solution and process – implementation Case study – APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Chapter 6: AI Project Management Chevron down icon Chevron up icon
Chapter 6: AI Project Management The problem – pain points and challenges The solution and its implementation Hypothetical case study – APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Chapter 7: Understanding Deterministic, Probabilistic, and Generative AI Chevron down icon Chevron up icon
Chapter 7: Understanding Deterministic, Probabilistic, and Generative AI The problem – pain points and challenges The solution and implementation Hypothetical case study – APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Chapter 8: AI Agents and Agentic Systems Chevron down icon Chevron up icon
Chapter 8: AI Agents and Agentic Systems What are AI agents? The problem – pain points and challenges The solution and implementation Hypothetical case study – APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Chapter 9: Designing AI Systems Chevron down icon Chevron up icon
Chapter 9: Designing AI Systems The problem – pain points and challenges The solution – step-by-step implementation Hypothetical case study – APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Chapter 10: Training AI Models Chevron down icon Chevron up icon
Chapter 10: Training AI Models AI model training – from data to insights The problem – pain points and challenges The solution and process – step-by-step implementation Hypothetical case study – APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Chapter 11: Deploying AI Solutions Chevron down icon Chevron up icon
Chapter 11: Deploying AI Solutions The problem – pain points and challenges The solution and implementation process Hypothetical case study – APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Part 3: Governance, Ethics, Security, and Compliance Chevron down icon Chevron up icon
Part 3: Governance, Ethics, Security, and Compliance
Chapter 12: AI Governance and Ethics Chevron down icon Chevron up icon
Chapter 12: AI Governance and Ethics The problem – pain points and challenges The solution and process – implementation Hypothetical case study – APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Chapter 13: Security in AI Systems Chevron down icon Chevron up icon
Chapter 13: Security in AI Systems The problem – pain points and challenges The solution and process – implementation Hypothetical case study – APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Chapter 14: Privacy in the Age of AI Chevron down icon Chevron up icon
Chapter 14: Privacy in the Age of AI The problem – pain points and challenges The solution and process – implementation Hypothetical case study – APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Chapter 15: AI Compliance Chevron down icon Chevron up icon
Chapter 15: AI Compliance The problem – pain points and challenges The solution and process – implementation Hypothetical case study – APEX Manufacturing and Distribution Reflection and practical next steps Summary Questions References
Part 4: Empowering AI Leadership with Practical Tools and Insights Chevron down icon Chevron up icon
Part 4: Empowering AI Leadership with Practical Tools and Insights
Chapter 16: Conclusion Chevron down icon Chevron up icon
Chapter 16: Conclusion The transformative power of AI Key themes and insights The profound impact on business strategies The road ahead for Chief AI Officers Summary References
Chapter 17: Appendix Chevron down icon Chevron up icon
Chapter 17: Appendix Glossary of AI terms Recommended readings and resources Templates and frameworks
Assessments Chevron down icon Chevron up icon
Chapter 1 – Why Every Company Needs a Chief AI Officer Chapter 2 – Key Responsibilities of a Chief AI Officer Chapter 3 – Crafting a Winning AI Strategy Chapter 4 – Building High-Performing AI Teams Chapter 5 – Data – the Lifeblood of AI Chapter 6 – AI Project Management Chapter 7 – Understanding Deterministic, Probabilistic, and Generative AI Chapter 8 – AI Agents and Agentic Systems Chapter 9 – Designing AI Systems Chapter 10 – Training AI Models Chapter 11 – Deploying AI Solutions Chapter 12 – AI Governance and Ethics Chapter 13 – Security in AI Systems Chapter 14 – Privacy in the Age of AI Chapter 15 – AI Compliance
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