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Fundamentals of Generative AI Principles of Agentic Systems Essential Components of Intelligent Agents Reflection and Introspection in Agents Enabling Tool Use and Planning in Agents Exploring the Coordinator, Worker, and Delegator Approach Effective Agentic System Design Techniques Building Trust in Generative AI Systems Managing Safety and Ethical Considerations Common Use Cases and Applications Conclusion and Future Outlook

Understanding the LLM Twin Concept and Architecture Tooling and Installation Data Engineering RAG Feature Pipeline Supervised Fine-Tuning Fine-Tuning with Preference Alignment Evaluating LLMs Inference Optimization RAG Inference Pipeline Inference Pipeline Deployment MLOps and LLMOps

The Rise of Generative AI: From Language Models to Agents First Steps with LangChain Building Workflows with LangGraph Building Intelligent RAG Systems with LangChain Building Intelligent Agents Advanced Applications and Multi-Agent Systems Software Development and Data Analysis Agents Evaluation and Testing Observability and Production Deployment The Future of LLM Applications

Vectors and vector spaces The geometric structure of vector spaces Linear algebra in practice spaces: measuring distances Linear transformations Matrices and equations Eigenvalues and eigenvectors Matrix factorizations Matrices and graphs Functions Numbers, sequences, and series Topology, limits, and continuity Differentiation Optimization Integration Multivariable functions Derivatives and gradients Optimization in multiple variables What is probability? Random variables and distributions The expected value The maximum likelihood estimation It's just logic The structure of mathematics Basics of set theory Complex numbers

A Gentle Introduction to Python Built-In Data Types Conditionals and Iteration Functions, the Building Blocks of Code Comprehensions and Generators OOP, Decorators, and Iterators Exceptions and Context Managers Files and Data Persistence Cryptography and Tokens Testing Debugging and Profiling Introduction to Type Hinting Data Science in Brief Introduction to API Development CLI Applications Packaging Python Applications Programming Challenges

Introduction to Generative AI OpenAI and ChatGPT: Beyond the Market Hype Understanding Prompt Engineering Boosting Day-to-Day Productivity with ChatGPT Developing the Future with ChatGPT Mastering Marketing with ChatGPT Research Reinvented with ChatGPT Unleashing Creativity Visually with ChatGPT Exploring GPTs Leveraging OpenAI Models for Enterprise- Scale Applications Epilogue and Final Thoughts

Introducing LLMs, RAGs, and Neo4j Knowledge Graphs Demystifying RAG Building a Foundational Understanding of Knowledge Graph for Intelligent Applications Building Your Neo4j Graph with Movies Dataset Implementing Powerful Search Functionalities with Neo4j and Haystack Exploring Advanced Knowledge Graph Capabilities Introducing the Neo4j Spring AI and LangChain4j Frameworks for Building Recommendation Systems Constructing a Recommendation Graph with H&M Personalization Dataset Integrating LangChain4j and SpringAI with Neo4j Creating an Intelligent Recommendation System Choosing the Right Cloud Platform for GenAI Applications Deploying your Application on Cloud Epilogue

Introduction to LLM Design Patterns Data Cleaning for LLM Training Data Augmentation Handling Large Datasets for LLM Training Data Versioning Dataset Annotation and Labeling Training Pipeline Hyperparameter Tuning Regularization Checkpointing and Recovery Fine-Tuning Model Pruning Quantization Evaluation Metrics Cross-Validation Interpretability Fairness and Bias Detection Adversarial Robustness Reinforcement Learning from Human Feedback Chain-of-Thought Prompting Tree-of-Thoughts Prompting Reasoning and Acting Reasoning WithOut Observation Reflection Techniques Automatic Multi-Step Reasoning and Tool Use Retrieval-Augmented Generation Graph-Based RAG Advanced RAG Evaluating RAG Systems Agentic Patterns

Introduction to Generative AI: Drawing Data from Models Building Blocks of Deep Neural Networks The Rise of Methods for Text Generation NLP 2.0: Using Transformers to Generate Text LLM Foundations Open-Source LLMs Prompt Engineering LLM Toolbox LLM Optimization Techniques Emerging Applications in Generative AI Neural Networks Using VAEs Image Generation with GANs Style Transfer with GANs Deepfakes with GANs Diffusion Models and AI Art

Principles and Foundations of IoT and AI Data Access and Distributed Processing for IoT Machine Learning for IoT Deep Learning for IoT Optimization Techniques in IoT Reinforcement Learning for IoT Generative Adversarial Networks for IoT Distributed AI for IoT AI Cloud Platforms for IoT Deep Learning for Time Series Data from IoT AI for Video and Image Data from IoT AI for Text, Audio and Speech Data from IoT AI for Personal and Home IoT AI for Industrial IoT AI for Smart Cities IoT