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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 | 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 Subscribe to AI_Distilled, the go-to newsletter for AI professionals
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 Subscribe to AI_Distilled, the go-to newsletter for AI professionals
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 Subscribe to AI_Distilled, the go-to newsletter for AI professionals
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 Subscribe to AI_Distilled, the go-to newsletter for AI professionals
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 Subscribe to AI_Distilled, the go-to newsletter for AI professionals
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 Subscribe to AI_Distilled, the go-to newsletter for AI professionals
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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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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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
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 LangChain
Generative AI with LangChain
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By Ben Auffarth
May 2025 | 476 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
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Index Download a free PDF copy of this book Stay connected with Packt’s Generative AI community
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Building Neo4j-Powered Applications with LLMs
Building Neo4j-Powered Applications with LLMs
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By Ravindranatha Anthapu
June 2025 | 312 pages
Icon Design vector search and recommendation systems with LLMs using Neo4j GenAI, Haystack, Spring AI, and LangChain4j
Icon Apply best practices for graph exploration, modeling, reasoning, and performance optimization
Icon Build and consume Neo4j knowledge graphs and deploy your GenAI apps to Google Cloud
Icon Purchase of the print or Kindle book includes a free PDF eBook
Part: 1 Introducing RAG and Knowledge Graphs for LLM Grounding Chevron down icon Chevron up icon
Part: 1 Introducing RAG and Knowledge Graphs for LLM Grounding Stay tuned
Introducing LLMs, RAGs, and Neo4j Knowledge Graphs Chevron down icon Chevron up icon
Introducing LLMs, RAGs, and Neo4j Knowledge Graphs Outlining the evolution of GenAI through the lens of LLMs Understanding the importance of RAGs and knowledge graphs in LLMs Introducing Neo4j knowledge graphs Summary
Demystifying RAG Chevron down icon Chevron up icon
Demystifying RAG Technical requirements Understanding the power of RAG Deconstructing the RAG flow Retrieving external information for your RAG Building an end-to-end RAG flow Summary
Building a Foundational Understanding of Knowledge Graph for Intelligent Applications Chevron down icon Chevron up icon
Building a Foundational Understanding of Knowledge Graph for Intelligent Applications Technical requirements Understanding the importance of graph data modeling Combining the power of RAG and Neo4j knowledge graphs with GraphRAG Enhancing knowledge graphs Summary
Part 2: Integrating Haystack with Neo4j: A Practical Guide to Building AI-Powered Search Chevron down icon Chevron up icon
Part 2: Integrating Haystack with Neo4j: A Practical Guide to Building AI-Powered Search Stay tuned
Building Your Neo4j Graph with Movies Dataset Chevron down icon Chevron up icon
Building Your Neo4j Graph with Movies Dataset Technical requirements Design considerations for a Neo4j graph for an efficient search Utilizing a movies dataset Building your movie knowledge graph with code examples Beyond the basics: advanced Cypher techniques for complex graph structures Summary
Implementing Powerful Search Functionalities with Neo4j and Haystack Chevron down icon Chevron up icon
Implementing Powerful Search Functionalities with Neo4j and Haystack Technical requirements Connecting Haystack to Neo4j for advanced vector search Building a search-driven chatbot with Gradio and Haystack Fine-tuning your Haystack integration Summary
Exploring Advanced Knowledge Graph Capabilities with Neo4j Chevron down icon Chevron up icon
Exploring Advanced Knowledge Graph Capabilities with Neo4j Technical requirements Exploring advanced Haystack functionalities for knowledge exploration Graph reasoning with Haystack Scaling your Haystack and Neo4j integration Best practices for maintaining and monitoring your AI-powered search system Summary
Part 3: Building an Intelligent Recommendation System with Neo4j, Spring AI, and LangChain4j Chevron down icon Chevron up icon
Part 3: Building an Intelligent Recommendation System with Neo4j, Spring AI, and LangChain4j Stay tuned
Introducing the Neo4j Spring AI and LangChain4j Frameworks for Building Recommendation Systems Chevron down icon Chevron up icon
Introducing the Neo4j Spring AI and LangChain4j Frameworks for Building Recommendation Systems Technical requirements Understanding extended Neo4j capabilities to build intelligent applications Personalizing recommendations Introducing Neo4j’s LangChain4j and Spring AI frameworks Overview of an intelligent recommendation system in Neo4j GenAI ecosystem Summary
Constructing a Recommendation Graph with H&M Personalization Dataset Chevron down icon Chevron up icon
Constructing a Recommendation Graph with H&M Personalization Dataset Technical requirements Modeling the recommendation graph with the H&M personalization dataset Optimizing for recommendations: best practices in graph modeling Summary
Integrating LangChain4j and Spring AI with Neo4j Chevron down icon Chevron up icon
Integrating LangChain4j and Spring AI with Neo4j Technical requirements Setting up LangChain4j and Spring AI Building your recommendation engine with LangChain4j Building your recommendation engine with Spring AI Fine-tuning your recommendation system Summary
Creating an Intelligent Recommendation System Chevron down icon Chevron up icon
Creating an Intelligent Recommendation System Technical requirements Improving recommendations with GDS algorithms Understanding the power of communities Combining collaborative filtering and content-based approaches Summary
Part 4: Deploying Your GenAI Application in the Cloud Chevron down icon Chevron up icon
Part 4: Deploying Your GenAI Application in the Cloud Stay tuned
Choosing the Right Cloud Platform for GenAI Applications Chevron down icon Chevron up icon
Choosing the Right Cloud Platform for GenAI Applications Understanding cloud computing options for GenAI applications Picking a cloud platform for GenAI applications: key considerations Making the right choice: a decision-making framework for selecting your cloud platform Summary
Deploying Your Application on the Google Cloud Chevron down icon Chevron up icon
Deploying Your Application on the Google Cloud Technical requirements Preparing your Haystack chatbot for deployment Containerizing the application with Docker Setting up a Google Cloud project and services Deploying to Google Cloud Run Testing and verifying the deployment on Google Cloud Preparing for deployment in production: key considerations Summary
Epilogue Chevron down icon Chevron up icon
Epilogue The combined power of GenAI and Neo4j Beyond the book: exploring resources for continued learning Closing remarks Stay tuned
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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LLM Design Patterns
LLM Design Patterns
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By Ken Huang
May 2025 | 534 pages
Icon Learn comprehensive LLM development, including data prep, training pipelines, and optimization
Icon Explore advanced prompting techniques, such as chain-of-thought, tree-of-thought, RAG, and AI agents
Icon Implement evaluation metrics, interpretability, and bias detection for fair, reliable models
Icon Print or Kindle purchase includes a free PDF eBook
Part 1: Introduction and Data Preparation Chevron down icon Chevron up icon
Part 1: Introduction and Data Preparation
Chapter 1: Introduction to LLM Design Patterns Chevron down icon Chevron up icon
Chapter 1: Introduction to LLM Design Patterns Understanding LLMs Understanding design patterns Design patterns for LLM development Summary
Chapter 2: Data Cleaning for LLM Training Chevron down icon Chevron up icon
Chapter 2: Data Cleaning for LLM Training Understanding the importance of clean data Common data quality issues in language datasets Text preprocessing techniques for LLMs Handling multilingual and code-mixed data Deduplication strategies for large text corpora Automated data cleaning pipelines Data validation and quality assurance Summary
Chapter 3: Data Augmentation Chevron down icon Chevron up icon
Chapter 3: Data Augmentation Text data augmentation techniques Leveraging existing LLMs for data generation Multilingual data augmentation strategies Semantic preservation in text augmentation Balancing augmentation and data quality Evaluating the impact of data augmentation Summary
Chapter 4: Handling Large Datasets for LLM Training Chevron down icon Chevron up icon
Chapter 4: Handling Large Datasets for LLM Training Challenges of large datasets Data sampling techniques Distributed data processing Data sharding and parallelization strategies Efficient data storage formats Streaming data processing for continuous LLM training Memory-efficient data loading techniques Summary
Chapter 5: Data Versioning Chevron down icon Chevron up icon
Chapter 5: Data Versioning Understanding the need for data versioning Data versioning strategies for large language datasets Tools for data versioning Integrating data versioning in training workflows Version control for text corpora Managing dataset variants and experiments Best practices for data versioning Summary
Chapter 6: Dataset Annotation and Labeling Chevron down icon Chevron up icon
Chapter 6: Dataset Annotation and Labeling The importance of quality annotations Annotation strategies for different tasks Tools and platforms for large-scale text annotation Managing annotation quality Crowdsourcing annotations – benefits and challenges Semi-automated annotation techniques Scaling annotation processes for massive language datasets Annotation biases and mitigation strategies Summary
Part 2: Training and Optimization of Large Language Models Chevron down icon Chevron up icon
Part 2: Training and Optimization of Large Language Models
Chapter 7: Training Pipeline Chevron down icon Chevron up icon
Chapter 7: Training Pipeline Components of a training pipeline Data input and preprocessing LLM architecture design considerations Loss functions and optimization strategies Logging Pipeline modularity and reusability Scaling your training pipeline for larger models Summary
Chapter 8: Hyperparameter Tuning Chevron down icon Chevron up icon
Chapter 8: Hyperparameter Tuning Understanding hyperparameters Manual versus automated tuning Grid and random search Bayesian optimization Population-based methods Multi-objective hyperparameter optimization Hyperparameter tuning at scale – challenges and solutions Summary
Chapter 9: Regularization Chevron down icon Chevron up icon
Chapter 9: Regularization L2 regularization (Ridge regression) Dropout Layer-wise adaptive regularization Gradient clipping and noise injection Regularization in transfer learning and fine-tuning scenarios Emerging regularization techniques Summary
Chapter 10: Checkpointing and Recovery Chevron down icon Chevron up icon
Chapter 10: Checkpointing and Recovery Why is checkpointing important? Checkpoint frequency and storage strategies Efficient checkpoint formats Recovering from failures Checkpointing in distributed LLM training Version control for LLM checkpoints Automated checkpointing and recovery systems Summary
Chapter 11: Fine-Tuning Chevron down icon Chevron up icon
Chapter 11: Fine-Tuning Implementing transfer learning and fine-tuning Strategies for freezing and unfreezing layers Learning rate scheduling Domain-specific fine-tuning techniques Few-shot and zero-shot fine-tuning Continual fine-tuning and catastrophic forgetting Summary
Chapter 12: Model Pruning Chevron down icon Chevron up icon
Chapter 12: Model Pruning Magnitude-based pruning Structured versus unstructured pruning Iterative pruning techniques Pruning during training versus post-training pruning Balancing pruning and model performance Combining pruning with other compression techniques Summary
Chapter 13: Quantization Chevron down icon Chevron up icon
Chapter 13: Quantization Understanding the basics Mixed-precision quantization Hardware-specific considerations Comparing quantization strategies Combining quantization with other optimization techniques Summary
Part 3: Evaluation and Interpretation of Large Language Models Chevron down icon Chevron up icon
Part 3: Evaluation and Interpretation of Large Language Models
Chapter 14: Evaluation Metrics Chevron down icon Chevron up icon
Chapter 14: Evaluation Metrics NLU benchmarks Reasoning and problem-solving metrics Coding and programming evaluation Conversational ability assessment Commonsense and general knowledge benchmarks Other commonly used benchmarks Developing custom metrics and benchmarks Interpreting and comparing LLM evaluation results Summary
Chapter 15: Cross-Validation Chevron down icon Chevron up icon
Chapter 15: Cross-Validation Pre-training and fine-tuning data splits Few-shot and zero-shot evaluation strategies Domain and task generalization Continual learning evaluation Cross-validation challenges and best practices Summary
Chapter 16: Interpretability Chevron down icon Chevron up icon
Chapter 16: Interpretability Attention visualization techniques Probing methods Explaining LLM predictions with attribution methods Interpretability in transformer-based LLMs Mechanistic interpretability Trade-offs between interpretability and performance Summary
Chapter 17: Fairness and Bias Detection Chevron down icon Chevron up icon
Chapter 17: Fairness and Bias Detection Types of bias Fairness metrics for LLM text generation and understanding Detecting bias Debiasing strategies Fairness-aware training Ethical considerations Summary
Chapter 18: Adversarial Robustness Chevron down icon Chevron up icon
Chapter 18: Adversarial Robustness Types of textual adversarial attacks Adversarial training techniques Evaluating robustness Trade-offs in the adversarial training of LLMs Real-world implications Summary
Chapter 19: Reinforcement Learning from Human Feedback Chevron down icon Chevron up icon
Chapter 19: Reinforcement Learning from Human Feedback Components of RLHF systems Scaling RLHF Limitations of RLHF in language modeling Applications of RLHF Summary
Part 4: Advanced Prompt Engineering Techniques Chevron down icon Chevron up icon
Part 4: Advanced Prompt Engineering Techniques
Chapter 20: Chain-of-Thought Prompting Chevron down icon Chevron up icon
Chapter 20: Chain-of-Thought Prompting Designing effective CoT prompts Using CoT prompting for problem solving Combining CoT prompting with other techniques Evaluating CoT prompting outputs Limitations of CoT prompting Future directions Summary
Chapter 21: Tree-of-Thoughts Prompting Chevron down icon Chevron up icon
Chapter 21: Tree-of-Thoughts Prompting Designing ToT prompts Search strategies Pruning and evaluation Applying ToT to solve a multi-step problem Challenges in implementation Future directions Summary
Chapter 22: Reasoning and Acting Chevron down icon Chevron up icon
Chapter 22: Reasoning and Acting Implementing ReAct in LangChain Building ReAct agents with LangChain’s Expression Language Completing tasks and solving problems Evaluating ReAct’s performance Safety, control, and ethical considerations Limitations and future directions Summary
Chapter 23: Reasoning WithOut Observation Chevron down icon Chevron up icon
Chapter 23: Reasoning WithOut Observation Implementing ReWOO with LangGraph Advantages of ReWOO Evaluating quality and ethical considerations Future directions Summary
Chapter 24: Reflection Techniques Chevron down icon Chevron up icon
Chapter 24: Reflection Techniques Designing prompts for self-reflection Implementing iterative refinement Correcting errors Evaluating the impact of reflection Challenges in implementing effective reflection Future directions Summary
Chapter 25: Automatic Multi-Step Reasoning and Tool Use Chevron down icon Chevron up icon
Chapter 25: Automatic Multi-Step Reasoning and Tool Use Designing prompts for complex task decomposition Integrating external tools Implementing automatic tool selection and use Complex problem solving Evaluating multi-step reasoning and tool use Challenges and future directions Summary
Part 5: Retrieval and Knowledge Integration in Large Language Models Chevron down icon Chevron up icon
Part 5: Retrieval and Knowledge Integration in Large Language Models
Chapter 26: Retrieval-Augmented Generation Chevron down icon Chevron up icon
Chapter 26: Retrieval-Augmented Generation Building a simple RAG system for LLMs Embeddings and indexing for retrieval in LLM applications Query formulation strategies in LLM-based RAG Integrating retrieved information with LLM generation Challenges and opportunities in RAG for LLMs Summary
Chapter 27: Graph-Based RAG Chevron down icon Chevron up icon
Chapter 27: Graph-Based RAG Introduction to graph-based knowledge representation for LLMs Designing graph RAG architectures for LLMs Graph embedding techniques for LLM retrieval Query expansion using graph structures in LLMs Applications and use cases of graph RAG in LLMs Challenges and future directions in graph-based RAG Summary
Chapter 28: Advanced RAG Chevron down icon Chevron up icon
Chapter 28: Advanced RAG Multi-step and iterative retrieval techniques for LLMs Adaptive retrieval based on context and task in LLMs Meta-learning for improved retrieval in LLMs Combining RAG with other LLM prompting techniques Handling ambiguity and uncertainty in LLM-based RAG Scaling RAG to very large knowledge bases Future directions in RAG research for LLMs Summary
Chapter 29: Evaluating RAG Systems Chevron down icon Chevron up icon
Chapter 29: Evaluating RAG Systems Challenges in evaluating RAG systems for LLMs Metrics for assessing retrieval quality in LLM-based RAG Considerations for retrieval metrics in RAG Evaluating the relevance of retrieved information for LLMs Measuring the impact of retrieval on LLM generation End-to-end evaluation of RAG systems in LLMs Human evaluation techniques for LLM-based RAG Benchmarks and datasets for RAG evaluation Summary
Chapter 30: Agentic Patterns Chevron down icon Chevron up icon
Chapter 30: Agentic Patterns Introduction to agentic AI systems based on LLMs Goal-setting and planning in LLM-based agents Implementing memory and state management for LLM agents Decision-making and action selection in LLM-based agents Learning and adaptation in agentic LLM systems Ethical considerations and safety in LLM-based agentic AI Future prospects of agentic AI using LLMs Summary Future directions in LLM patterns and their development
Index Chevron down icon Chevron up icon
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MCP for Leaders - Architecting Context-Driven AI
MCP for Leaders - Architecting Context-Driven AI
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By Vivian Aranha
June 2025 | 170 pages
Icon Dive into leadership-focused strategies for integrating MCP into organizational workflows.
Icon Understand MCP tools, deployment options, and best practices for security and data ownership.
Icon Get access to executive-level case studies and vision planning workshops to implement MCP.
Executive Introduction to MCP Chevron down icon Chevron up icon
What Is MCP? Why MCP Matters in the Age of AI Agents Business Impact: From Static Models to Dynamic Contextual Systems Real-World Use Cases Across Industries How MCP Fits into Your AI Strategy
Core Concepts Behind MCP Chevron down icon Chevron up icon
Context in AI: What It Means and Why It's Crucial MCP Architecture Overview (No-Code Explanation) Context Routing, Agents, and Protocol Layers How MCP Enables Multi-Modal Intelligence (Text, Voice, Code, More)
Business Applications of MCP Chevron down icon Chevron up icon
Enhancing Decision Intelligence with MCP Autonomous Agents for Operations, HR, and Customer Service Using MCP in RAG (Retrieval-Augmented Generation) Systems Compliance, Auditing, and Explainability via Context-Aware Agents
Leadership Use Cases and Strategies Chevron down icon Chevron up icon
Building AI-Ready Teams with MCP Principles Choosing Between Internal vs External MCP Implementations MCP for Strategic AI Governance Budgeting and ROI: MCP Cost vs Value
Tooling and Ecosystem for Executives Chevron down icon Chevron up icon
Overview of MCP Tooling (e.g., LangGraph, Firecrawl, Chroma) Open-Source vs Enterprise Solutions Integration with Existing Systems (CRM, ERP, etc.) Security and Data Ownership in Local MCP Deployments
Case Studies and Vision Planning Chevron down icon Chevron up icon
Case Study: MCP in a Fortune 500 Enterprise Case Study: Local MCP for Confidential Document Q&A Vision Workshop: Designing Your First Context-Aware AI Initiative Executive Roadmap: Becoming a Context-Driven Organization
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Train Large Language Models Faster - Parallelism Deep Dive
Train Large Language Models Faster - Parallelism Deep Dive
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By Paulo Dichone
June 2025 | 530 pages
Icon Learn data parallelism, model parallelism, hybrid, pipeline, and tensor parallelism strategies
Icon Implement strategies in real-world scenarios, from small model training on MNIST to advanced parallelism with multiple GPUs
Icon Master checkpointing, storage, and failure management in distributed LLM training
Introduction Chevron down icon Chevron up icon
Introduction & What Is This Course About Course Structure DEMO - What You'll Build in This Course
Strategies for Parallelizing LLMS - Deep Dive Chevron down icon Chevron up icon
What is Parallelism and Why it Matters Understanding the Single GPU Strategy Understanding the Parallel Strategy and Advantages Parallelism vs Single GPU - Summary
IT Fundamental Concepts Chevron down icon Chevron up icon
IT Fundamentals - Introduction Introduction to Cloud Computing and Traditional IT What is a Computer - CPU and RAM Overview Data Storage and File Systems OS File System Structure LAN Introduction What is the Internet Internet Communication Deep Dive Understanding Servers and Clients GPUs - Overview
GPU Architecture for LLM Training Deep Dive Chevron down icon Chevron up icon
GPU Architecture for LLM Training Why this Architecture Excels
Deep and Machine Learning - Deep Dive Chevron down icon Chevron up icon
Machine and Deep Learning Introduction Deep and Machine Learning - Overview and Breakdown Deep Learning Key Aspects Deep Neural Networks - Deep Dive The Single Neuron Computation - Deep Dive Weights Activation Functions - Deep Dive Deep Learning - Summary Machine Learning Introduction - ML vs DL Learning Types and Full ML & DL Analogy Example DL and ML Comparative Capabilities - Summary
Large Language Models - Fundamentals of AI and LLMs Chevron down icon Chevron up icon
Introduction The Transformer Architecture Fundamentals The Self-Attention Mechanism - Analogy The Transformer Architecture Animation The Transformer Library - Deep Dive
Parallel Computing Fundamentals & Parallelism in LLM Training Chevron down icon Chevron up icon
Parallel Computing Introduction - Key Concepts Parallel Computing Fundamentals and Scaling Laws - Deep Dive
Types of Parallelism in LLM Training - Data, Model, and Hybrid Parallelism Chevron down icon Chevron up icon
Types of Parallelism in LLM Training Data Parallelism - How It Works Data Parallelism Advantages for LLM Training Real-world Example - Data Parallelism in GPT-3 Training Model Parallelism and Tensor Parallelism and Layer Parallelism - Deep Dive LLM Relevance and Implementation Model vs Data Parallelism Key Differences Highlighted - Data vs Model Parallelism Data vs Model Parallelism Hybrid Parallelism - Animation Hybrid Parallelism - What is It and Motivation
Types of Parallelism - Pipeline and Tensor Parallelism Chevron down icon Chevron up icon
Pipeline Parallelism Overview Pipeline Parallelism Key Concepts and How it Works - Step by Step Pipeline Bubbles Key Concepts Pipeline Schedules Key Concepts Activation Recomputation - Overview and Introduction Neural Network and Activation and Backward and Forward Passes - Full Dive Understanding Activation Recomputation vs Standard Training - Deep Dive Demo - Activation Recomputation Visualization Activation Recomputation vs Standard Approach Benefits of Activation Recomputation and Implementation Strategies Pipeline Parallelism Implementation Frameworks and Key Takeaways
Tensor Parallelism - Deep Dive Chevron down icon Chevron up icon
What is Tensor Parallelism and Why - Benefits Tensor Parallel Pizza Making Analogy Tensors and Partitioning Strategies - Deep Dive Tensor Communication Patterns - Deep Dive Device Mesh Communication Pattern - Deep Dive How Components Work Together in Distributed LLM Training Understanding Tensor Parallelism with LEGO Bricks Animation Demo Putting it All Together - All Strategies in LLM Training
HANDS-ON: Strategies for Parallelism - Data Parallelism Deep Dive Chevron down icon Chevron up icon
Strategies for Parallelizing LLMs - Hands-on Introduction Pytorch - LLM Training Library Overview The Transformers Library - Overview Numpy Overview TorchVision and TorchDistributed Overview DeepSpeed and Megatron-LM - Overview Datasets and Why this Toolkit HANDS-On: Data Parallelism - Training a Small Model - MNIST Dataset Testing Pseudo Data Parallelism Trained Model HANDS-ON: Data Parallelism - Colab - Full Demo Data Parallelism - Simulated Parallelism on GPU Takeaways
HANDS-ON: Data Parallelism w/ WikiText Dataset & DeepSpeed Mem. Optimization Chevron down icon Chevron up icon
Hands-on: Data Parallelism - Wikitext-2 Dataset DeepSpeed - Full Dive Hands-on: Data Parallelism with DeepSpeed Optimization
Running TRUE Parallelism on Multiple GPU Systems - Runpod.io Chevron down icon Chevron up icon
Setup Runpod.io Environment Overview Runpod SSH Setup Setting up Runpod Parallelism in JupyterNotebook HANDS-ON - Parallelism with IMDB Dataset - Deep Dive - True Parallelism Runpod Cleanup
Fault Tolerance and Scalability & Advanced Checkpointing Strategies - Deep Dive Chevron down icon Chevron up icon
Fault Tolerance Introduction & Types of Failures in Distributed LLM Training Strategies for Fault Tolerance Checkpointing in LLM Training - Animation Basic Checkpointing in LLM Training Incremental Checkpointing in LLM Training Asynchronous Checkpointing in LLM Training Multi-level Checkpointing in LLM Training - Animation Checkpoint Storage Considerations - Deep Dive Implementing a Hybrid Approach - Performance, Failure, Optimizations - Full Dive Checkpoint Storage Strategy - Summary
Advanced Topics and Emerging Trends Chevron down icon Chevron up icon
Advanced Topics and Emerging Trends
Wrap up and Next Steps Chevron down icon Chevron up icon
Course Summary and Next Steps
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Deploy Intelligent AI Agents with Amazon Bedrock – Step-by-Step
Deploy Intelligent AI Agents with Amazon Bedrock – Step-by-Step
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By Paulo Dichone
June 2025 | 124 pages
Icon Amazon Bedrock agent workflows with hands-on implementation
Icon Full-stack deployment using Lambda and frontend integration
Icon Real-world capstone project building a functional web scraper agent
Introduction Chevron down icon Chevron up icon
Introduction Course Structure AWS Account Knowledge – Pre-reqs What You'll Build by the End
Amazon Bedrock Deep Dive Chevron down icon Chevron up icon
Amazon Bedrock Deep Dive – What Is It and Benefits Amazon Bedrock Key Features and Security, Privacy, and Safety Amazon Bedrock Pricing Hands-on: Amazon Bedrock Console Walkthrough – Playground Hands-on – Generate Images
Amazon Bedrock AI Agents – Deep Dive Chevron down icon Chevron up icon
Amazon Bedrock Agent Deep Dive – Basic Workflow Detailed Flow of Bedrock Agent – Full Overview Hands-on: Build Our First Amazon Bedrock Agent – Simple Agent Hands-on: Adding Knowledge Base and Testing It Hands-on: Action Groups – Adding a Lambda Function Amazon Bedrock Agent and Action Groups – Architectural Overview Amazon Bedrock Agent Architectural Overview and Animation Build-time vs. Runtime Operations – Overview Amazon Bedrock Agents – Creation Process & Benefits
Capstone Project – Web Scraper Agent – Full Workflow Chevron down icon Chevron up icon
Hands-on: Scraper Agent – Console Setup Hands-on: Create an Agent Alias – Agent Deployment Final – Scraper Agent Frontend Client
Wrapup Chevron down icon Chevron up icon
Congratulations
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AI & LLM Engineering Mastery - GenAI, RAG Complete Guide
AI & LLM Engineering Mastery - GenAI, RAG Complete Guide
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By Paulo Dichone
June 2025 | 1096 pages
Icon Complete coverage of GenAI, LLMs, RAG, and fine-tuning with real-world projects
Icon Practical hands-on coding with Transformers, OpenAI, and LangChain frameworks
Icon In-depth training on Prompt Engineering, memory, and API deployment techniques
Introduction Chevron down icon Chevron up icon
Introduction Demo: What You'll Build in This Course Course Structure How to Get the Most from This Course
Development Environment Setup Chevron down icon Chevron up icon
Development Environment Setup: Overview Install Python on Windows: For Windows Users Install Python on Mac: For Mac Users Download Visual Studio Code Install the Python Extension Pack for VS Code Running First Python Program in VS Code
Optional: Python Deep Dive—Master Python Fundamentals Chevron down icon Chevron up icon
What Is Python and Where It's Used? Python Compilation and Interpretation Process Declaring Variables in Python Data Types Python f-Strings Numbers: Integers and Floats Introduction to Lists: Accessing and Modifying Them f-Strings and Individual Values from a List Sorting a List and Getting a List Length Lists and Loops: Looping Through a List Making a List of Numbers with Loops and the Range Function Statistics Functions for Numbers Generate Even Numbers with the List and Range Important: Code Organization Note List Comprehension Tuples Branching: If Statements and Booleans The Elif and the in Keywords Hands-On: Using and and or Logical Operators and or Logical Operators Checking for Inequalities Hands-On: Inner If-Statements Data Structures: Dictionaries—Introduction and Declaring and Accessing Values Modifying a Dictionary Iterating Through a Dictionary Nested Dictionaries and Looping Through Them Looping Through a Dictionary with a List Inside User Input and While Loops: User Input—Introduction Hands-On: Odd or Even Number While Loops and Simple Quit Program Hands-On: Quiz Game Removing All Instances of Specific Values from a List Hands-On: Dream Travel Itinerary Program—Filling a Dictionary with User Input Functions: Introduction Passing Information to a Function (Parameters) Positional and Named Arguments Default Values: Parameters Return Values from a Function Hands-On: Returning an Integer and Intro to DocString Functions: Passing a List as Argument Passing an Arbitrary Number of Arguments to a Function Introduction to Modules: Importing Specific Functions from a Module Using the "as" as an Alias Classes and OOP: Object-Oriented Programming—The init and str Methods The init and str Methods Adding More Methods to the Class Setting a Default Value for an Attribute Modifying Class Attribute: Directly and with Methods Inheritance: Create an Ebook—Child Class Overriding Methods Creating and Importing from a Module The Object Class: Overview The Python Standard Library Random Module: Random Fruit Hands-On Hands-On: Random Fruit with Choice Module Method Using Datetime Module Writing and Reading Files: Do Useful Tasks with Python The Path Class and Reading a Text File Resolving Path: Reading from a Subdirectory with Path Path Properties Overview Writing to Text File with Path Read and Write to File Using the "with" Keyword Handling Exceptions The "FileNotFound" and "IndexError" Exception Types Custom Exception Creation and Handling JSON: Reading and Writing to a JSON File Hands-On: Writing and Reading Countries to JSON File Hands-On: File Organizer Python Virtual Environment and PIP Setting Up Virtual Environment and Installing a Package Hands-On: Watermarker Python Tool Building an Image Watermarker in Python: Part 1 Generating the Watermarked Images Reading CSV File: Introduction Getting the CSV Header Position Reading Data from a CSV Column Plotting a Graph with CSV Data
Understanding Deep and Machine Learning Chevron down icon Chevron up icon
Deep and Machine Learning Deep Dive: Overview and Breakdown Deep Learning Key Aspects Deep Neural Network Dissection: Full Dive with Analogies The Single Neuron Computation: Deep Dive Weights: Deep Dive Activation Functions: Deep Dive with Analogies Deep Learning Summary Machine Learning Introduction: Machine Learning vs. Deep Learning Learning Types: Education System Analogy Comparative Capabilities: Deep Learning, Machine Learning, and AI Summary
Generative AI: Architecture and Core Technologies Chevron down icon Chevron up icon
GenAI Introduction and Architecture Overview GenAI Key Technologies: Limitations and Challenges GenAI Key Components Overview and Summary
LLMs: Concepts, Architecture, and Hands-On Development Chevron down icon Chevron up icon
LLMs: Overview The Transformer Architecture: Fundamentals The Self-Attention Mechanism: Analogy The Transformers Library: Deep Dive Hands-On: Create a Simple LLM from the Transformers Library Hands-On: Enhanced Transformers LLM Open-Source vs. Closed-Source Models: Overview
OpenAI Models and Setup Chevron down icon Chevron up icon
Set Up OpenAI Account and API Key Using APIs Effectively in AI Projects Hands-On: Making Our First Call to OpenAI Model
Prompt Engineering: From Basics to Advanced Chevron down icon Chevron up icon
Prompt Engineering Introduction Prompt Engineering and Types: Why It Matters Hands-On: Simple Prompting Example Advanced Prompting Techniques and Challenges Hands-On: Few-Shots Prompting Hands-On: Zero-Shot Prompting Hands-On: Chain-of-Thought Prompting Hands-On: Instructional Prompting Hands-On: Role-Playing and Open-Ended Prompting Temperature and Top-p Sampling Hands-On: Prompt Techniques Combination and Streaming Prompt Engineering Summary and Takeaways
Context and Memory Management in LLMs Chevron down icon Chevron up icon
Hands-On: Context and Memory Management Overview What Is Context and Memory Management: Deep Dive Hands-On: Adding Memory and Context to Chatbox Summary
Logging in LLM Applications Chevron down icon Chevron up icon
Logging Introduction: What and the Why Logging in LLM Applications and Logging Life Cycle Hands-On: Chatbot with Logging Summary
Understanding Retrieval-Augmented Generation (RAG) Chevron down icon Chevron up icon
RAG Introduction: What Is It? RAG Key Components: The RAG Triad RAG vs. Pure GenAI Models RAG Deep Dive: Full Diagram Walkthrough RAG Benefits and Practical Applications RAG Challenges RAG Fundamentals: Takeaways and Summary
RAG PDF Workflow and UI Integration Chevron down icon Chevron up icon
Building a RAG Pipeline: Overview First RAG Workflow Architectural Diagram Setting Up the Embedding Model Class Hands-On: Building and Showcasing the RAG Workflow Hands-On: RAG Workflow with UI—Streamlit First RAG Pipeline Summary
Hands-On: PDF RAG System with Text Chunking Chevron down icon Chevron up icon
PDF RAG Workflow: Architecture Overview PDF and Chunk Processing and Chunk Overlap: Deep Dive Setting Up the SimpleRAGSystem Class and Methods Testing the PDF RAG System Simple PDF RAG Workflow: Summary
LangChain Fundamentals and Workflow Integration Chevron down icon Chevron up icon
LLM Frameworks Introduction: LangChain Fundamentals What Is LangChain and Main Components LangChain Setup and ChatModel Hands-On: LangChain ChatPromptTemplates Indexes, Retrievers, and Data Preparation: Overview Hands-On: LangChain TextLoaders Hands-On: Text Splitting and Cleaning Hands-On: Embeddings and Retriever with FAISS VectorStore LangChain TextSplitter: Deep Dive LangChain DirectoryLoader LangChain PDFLoader Hands-On: LangChain Chains Hands-On: Simple RAG System with Chat and LangChain Chains Hands-On: Full RAG System QA Bot Using LangChain
Hands-On: Building LLM Applications with LangChain Chevron down icon Chevron up icon
LLM Application: News Summarizer—Architectural Overview News Summarizer: Full Implementation LLM Application: YouTube Video Summarizer—Architectural Overview YouTube Video Summarizer and Q&A Dependency Setup YouTube Video Summarizer Class Setup and Walkthrough YouTube Video Summarizer Q&A: Testing the Workflow LLM Application: Voice Assistant RAG System—Architectural Overview Voice Assistant RAG System: Demo Voice Assistant RAG System: Walkthrough and Demo
Fine-Tuning LLMs Chevron down icon Chevron up icon
Fine-Tuning Introduction: Overview Fine-Tuning Techniques: Overview Fine-Tuning Comparison of Techniques Fine-Tuning General Process: Overview Fine-Tuning OpenAI Models Pricing Tokens and the Tokenizer OpenAI Tool Hands-On: Fine-Tuning an OpenAI Model—Full Walkthrough Creating a Chatbot with Our Fine-Tuned Model and Testing
LoRA-Based Fine-Tuning and Deployment Chevron down icon Chevron up icon
LoRA Introduction: Benefits LoRA Deep Analysis LoRA Implementation Strategy Workflow Hands-On: Training Models—LoRA and PEFT Running LoRA Model Fine-Tuning and Testing Creating an API Service to Interface with Our Fine-Tuned Models Testing Our LoRA Model API Endpoint Chatting with LoRA Fine-Tuned Models Full LoRA Workflow: Train and Chat with Fine-Tuned Models
Wrap-Up and Next Steps Chevron down icon Chevron up icon
Wrap-Up and Next Steps
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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
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
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
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
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
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
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
Making Decisions in Complex Environments with Reinforcement Learning Chevron down icon Chevron up icon
Making Decisions in Complex Environments with Reinforcement Learning Setting up the working environment Introducing OpenAI Gym and Gymnasium Introducing reinforcement learning with examples Solving the FrozenLake environment with dynamic programming Performing Monte Carlo learning Solving the Blackjack problem with the Q-learning algorithm Summary Exercises Join our book’s Discord space
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
Index Chevron down icon Chevron up icon
Index
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LLVM Code Generation
LLVM Code Generation
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By Quentin Colombet
May 2025 | 608 pages
Icon Understand the steps involved in generating assembly code from LLVM IR
Icon Learn the key constructs needed to leverage LLVM for your hardware or backend
Icon Strengthen your understanding with targeted exercises and practical examples in every chapter
Icon Purchase of the print or Kindle book includes a free PDF eBook
Getting Started with LLVM Chevron down icon Chevron up icon
Getting Started with LLVM
Building LLVM and Understanding the Directory Structure Chevron down icon Chevron up icon
Building LLVM and Understanding the Directory Structure Technical requirements Getting ready for LLVM’s world Building a compiler Building LLVM Testing a compiler Understanding the directory structure Summary Quiz time
Contributing to LLVM Chevron down icon Chevron up icon
Contributing to LLVM Reporting an issue Engaging with the community Contributing patches Summary Quiz time
Compiler Basics and How They Map to LLVM APIs Chevron down icon Chevron up icon
Compiler Basics and How They Map to LLVM APIs Technical requirements A word on APIs Understanding compiler jargon Working with basic structures Building your first IRs Summary Quiz time
Writing Your First Optimization Chevron down icon Chevron up icon
Writing Your First Optimization Technical requirements The concept of value Tackling optimizations Loops Writing a simple constant propagation optimization Summary Quiz time
Dealing with Pass Managers Chevron down icon Chevron up icon
Dealing with Pass Managers Technical requirements What is a pass? What is a pass manager? The legacy and new pass manager Creating a pass Inspecting the pass pipeline Your turn Summary Further reading Quiz time
TableGen – LLVM Swiss Army Knife for Modeling Chevron down icon Chevron up icon
TableGen – LLVM Swiss Army Knife for Modeling Technical requirements Getting started with TableGen The TableGen programming language Discovering a TableGen backend Debugging the TableGen framework Summary Further reading Quiz time
Middle-End: LLVM IR to LLVM IR Chevron down icon Chevron up icon
Middle-End: LLVM IR to LLVM IR
Understanding LLVM IR Chevron down icon Chevron up icon
Understanding LLVM IR Technical requirements Understanding the need for an IR Introducing LLVM IR Walking through an example Target-specific elements in LLVM IR Textual versus binary format LLVM IR API – cheat sheet Summary Further reading Quiz time
Survey of the Existing Passes Chevron down icon Chevron up icon
Survey of the Existing Passes Technical requirements How to find the unknown Survey of the helper passes Canonicalization passes Optimization passes Summary Further reading Quiz time
Introducing Target-Specific Constructs Chevron down icon Chevron up icon
Introducing Target-Specific Constructs Technical requirements Adding a new backend in LLVM Creating your own intrinsics Adding a target-specific TargetTransformInfo implementation Customizing the default middle-end pipeline Using the right abstraction Summary Further reading Quiz time
Hands-On Debugging LLVM IR Passes Chevron down icon Chevron up icon
Hands-On Debugging LLVM IR Passes Technical requirements The logging capabilities in LLVM Reducing the input IR size Using sanitizers A crash course on LLDB The LLVM code base through a debugger Summary Further reading Quiz time
Introduction to the Backend Chevron down icon Chevron up icon
Introduction to the Backend
Getting Started with the Backend Chevron down icon Chevron up icon
Getting Started with the Backend Technical requirements Introducing the Machine IR Here comes the Machine IR The Machine IR textual representation The anatomy of a MachineInstr instance Working with registers Creating MachineInstr objects Describing registers Describing instructions Summary Further reading Quiz time
Getting Started with the Machine Code Layer Chevron down icon Chevron up icon
Getting Started with the Machine Code Layer Technical requirements The use of the MC layer Connecting the MC layer Enabling MC-based tools Summary Quiz time
The Machine Pass Pipeline Chevron down icon Chevron up icon
The Machine Pass Pipeline Technical requirements The Machine pass pipeline at a glance Injecting passes Using the generic Machine optimizations Generic passes worth mentioning Summary Further reading Quiz time
LLVM IR to Machine IR Chevron down icon Chevron up icon
LLVM IR to Machine IR
Getting Started with Instruction Selection Chevron down icon Chevron up icon
Getting Started with Instruction Selection Technical requirements Overview of the instruction selection frameworks Which selector to use? Selectors’ inner workings Understanding the DAG representation Understanding the generic Machine IR Groundwork to connect the codegen pipeline Summary Further reading Quiz time
Instruction Selection: The IR Building Phase Chevron down icon Chevron up icon
Instruction Selection: The IR Building Phase Technical requirements Overview of the IR building Describing the calling convention Lowering the ABI with SDISel Lowering the ABI with FastISel Lowering the ABI with GlobalISel Summary Further reading Quiz time
Instruction Selection: The Legalization Phase Chevron down icon Chevron up icon
Instruction Selection: The Legalization Phase Technical requirements Legalization overview Legalization actions Legalization in SDISel Legalization in GlobalISel Summary Quiz time
Instruction Selection: The Selection Phase and Beyond Chevron down icon Chevron up icon
Instruction Selection: The Selection Phase and Beyond Technical requirements Register bank selection Instruction selection Finalizing the selection pipeline Optimizations Debugging the selectors Summary Quiz time
Final Lowering and Optimizations Chevron down icon Chevron up icon
Final Lowering and Optimizations
Instruction Scheduling Chevron down icon Chevron up icon
Instruction Scheduling Technical requirements Overview of the instruction scheduling framework The ScheduleDAGInstrs class Changing the scheduling algorithm The scheduling model Implementing your scheduling model Summary Quiz time
Register Allocation Chevron down icon Chevron up icon
Register Allocation Technical requirements Overview of register allocation in LLVM Enabling the register allocation infrastructure Introducing the slot indexes Introducing the live intervals Maintaining the live intervals Summary Further reading Quiz time
Lowering of the Stack Layout Chevron down icon Chevron up icon
Lowering of the Stack Layout Technical requirements Overview of stack lowering The lowering of the stack frame The expansion of the frame indices Summary Quiz time
Getting Started with the Assembler Chevron down icon Chevron up icon
Getting Started with the Assembler Technical requirements Overview of the lowering of a textual assembly file Assembling with the LLVM infrastructure Implementing an assembler Summary Further reading Quiz time
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
Index Chevron down icon Chevron up icon
Index
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C# 13 and .NET 9 – Modern Cross-Platform Development Fundamentals
C# 13 and .NET 9 – Modern Cross-Platform Development Fundamentals
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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!
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Index
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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
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Generative AI with Python and PyTorch
Generative AI with Python and PyTorch
Read now
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
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Practical Generative AI with ChatGPT
Practical Generative AI with ChatGPT
Read now
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
By Valentina Alto
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
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Expert reading lists

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Learning C# by Developing Games with Unity Hands-On Unity 2022 Game Development Beginning C++ Game Programming
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Getting Started with Machine Learning and Python Icon Building a Movie Recommendation Engine with Naïve Bayes Icon Predicting Online Ad Click-Through with Tree-Based Algorithms Icon Predicting Online Ad Click-Through with Logistic Regression Icon Predicting Stock Prices with Regression Algorithms Icon Predicting Stock Prices with Artificial Neural Networks Icon Mining the 20 Newsgroups Dataset with Text Analysis Techniques Icon Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling Icon Recognizing Faces with Support Vector Machine Icon Machine Learning Best Practices Icon Categorizing Images of Clothing with Convolutional Neural Networks Icon Making Predictions with Sequences Using Recurrent Neural Networks Icon Advancing Language Understanding and Generation with the Transformer Models Icon Building an Image Search Engine Using CLIP: a Multimodal Approach Icon Making Decisions in Complex Environments with Reinforcement Learning Icon Other Books You May Enjoy Icon Index Icon
Building Neo4j-Powered Applications with LLMs Building Neo4j-Powered Applications with LLMs
Jun-25
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Part: 1 Introducing RAG and Knowledge Graphs for LLM Grounding Icon Introducing LLMs, RAGs, and Neo4j Knowledge Graphs Icon Demystifying RAG Icon Building a Foundational Understanding of Knowledge Graph for Intelligent Applications Icon Part 2: Integrating Haystack with Neo4j: A Practical Guide to Building AI-Powered Search Icon Building Your Neo4j Graph with Movies Dataset Icon Implementing Powerful Search Functionalities with Neo4j and Haystack Icon Exploring Advanced Knowledge Graph Capabilities with Neo4j Icon Part 3: Building an Intelligent Recommendation System with Neo4j, Spring AI, and LangChain4j Icon Introducing the Neo4j Spring AI and LangChain4j Frameworks for Building Recommendation Systems Icon Constructing a Recommendation Graph with H&M Personalization Dataset Icon Integrating LangChain4j and Spring AI with Neo4j Icon Creating an Intelligent Recommendation System Icon Part 4: Deploying Your GenAI Application in the Cloud Icon Choosing the Right Cloud Platform for GenAI Applications Icon Deploying Your Application on the Google Cloud Icon Epilogue Icon Other Books You May Enjoy Icon Index Icon

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