Dilyan Grigorov – författare

This book is a complete, hands-on guide to designing, training, and deploying your own Large Language Models (LLMs)—from the foundations of tokenization to the advanced stages of fine-tuning and reinforcement learning. Written for developers, data scientists, and AI practitioners, it bridges core principles and state-of-the-art techniques, offering a rare, transparent look at how modern transformers truly work beneath the surface.Starting from the essentials, you’ll learn how to set up your environment with Python and PyTorch, manage datasets, and implement critical fundamentals such as tensors, embeddings, and gradient descent. You’ll then progress through the architectural heart of modern models, covering RMS normalization, rotary positional embeddings (RoPE), scaled dot-product attention, Grouped Query Attention (GQA), Mixture of Experts (MoE), and SwiGLU activations, each explored in depth and built step by step in code. As you advance, the book introduces custom CUDA kernel integration, teaching you how to optimize key components for speed and memory efficiency at the GPU level—an essential skill for scaling real-world LLMs. You’ll also gain mastery over the phases of training that define today’s leading models:Pretraining - Building general linguistic and semantic understanding.Midtraining - Expanding domain-specific capabilities and adaptability.Supervised Fine-Tuning (SFT) - Aligning behavior with curated, task-driven data.Reinforcement Learning from Human Feedback (RLHF) - Refining responses through reward-based optimization for human alignment.The final chapters guide you through dataset preparation, filtering, deduplication, and training optimization, culminating in model evaluation and real-world prompting with a custom TokenGenerator for text generation and inference.By the end of this book, you’ll have the knowledge and confidence to architect, train, and deploy your own transformer-based models, equipped with both the theoretical depth and practical expertise to innovate in the rapidly evolving world of AI.What You’ll LearnHow to configure and optimize your development environment using PyTorchThe mechanics of tokenization, embeddings, normalization, and attention mechanisms.How to implement transformer components like RMSNorm, RoPE, GQA, MoE, and SwiGLU from scratch.How to integrate custom CUDA kernels to accelerate transformer computations.The full LLM training pipeline: pretraining, midtraining, supervised fine-tuning, and RLHF.Techniques for dataset preparation, deduplication, model debugging, and GPU memory management.How to train, evaluate, and deploy a complete GPT-like architecture for real-world tasks.Who this book is for:Software developers, data scientists, machine learning engineers and AI enthusiasts looking to build their models from scratch.

Gain a solid foundation for Natural Language Processing (NLP) and Large Language Models (LLMs), emphasizing their significance in today’s computational world. This book is an introductory guide to NLP and LLMs with Python programming.The book starts with the basics of NLP and LLMs. It covers essential NLP concepts, such as text preprocessing, feature engineering, and sentiment analysis using Python. The book offers insights into Python programming, covering syntax, data types, conditionals, loops, functions, and object-oriented programming. Next, it delves deeper into LLMs, unraveling their complex components.You’ll learn about LLM elements, including embedding layers, feedforward layers, recurrent layers, and attention mechanisms. You’ll also explore important topics like tokens, token distributions, zero-shot learning, LLM hallucinations, and insights into popular LLM architectures such as GPT-4, BERT, T5, PALM, and others. Additionally, it covers Python libraries like Hugging Face, OpenAI API, and Cohere. The final chapter bridges theory with practical application, offering step-by-step examples of coded applications for tasks like text generation, summarization, language translation, question-answering systems, and chatbots.In the end, this book will equip you with the knowledge and tools to navigate the dynamic landscape of NLP and LLMs.What You’ll LearnUnderstand the basics of Python and the features of Python 3.11Explore the essentials of NLP and how do they lay the foundations for LLMs.Review LLM components.Develop basic apps using LLMs and Python.Who This Book Is ForData analysts, AI and Machine Learning Experts, Python developers, and Software Development Professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks.

Gain a solid foundation for Natural Language Processing (NLP) and Large Language Models (LLMs), emphasizing their significance in today’s computational world. This book is an introductory guide to NLP and LLMs with Python programming.

The book starts with the basics of NLP and LLMs. It covers essential NLP concepts, such as text preprocessing, feature engineering, and sentiment analysis using Python. The book offers insights into Python programming, covering syntax, data types, conditionals, loops, functions, and object-oriented programming. Next, it delves deeper into LLMs, unraveling their complex components.

You’ll learn about LLM elements, including embedding layers, feedforward layers, recurrent layers, and attention mechanisms. You’ll also explore important topics like tokens, token distributions, zero-shot learning, LLM hallucinations, and insights into popular LLM architectures such as GPT-4, BERT, T5, PALM, and others. Additionally, it covers Python libraries like Hugging Face, OpenAI API, and Cohere. The final chapter bridges theory with practical application, offering step-by-step examples of coded applications for tasks like text generation, summarization, language translation, question-answering systems, and chatbots.

In the end, this book will equip you with the knowledge and tools to navigate the dynamic landscape of NLP and LLMs.

What You’ll Learn

Understand the basics of Python and the features of Python 3.11Explore the essentials of NLP and how do they lay the foundations for LLMs.Review LLM components.Develop basic apps using LLMs and Python.

Who This Book Is For

Data analysts, AI and Machine Learning Experts, Python developers, and Software Development Professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks.

 

 

Harness the power of Large Language Models (LLMs) to build cutting-edge AI applications with Python and LangChain. This book provides a hands-on approach to understanding, implementing, and deploying LLM-powered solutions, equipping developers, data scientists, and AI enthusiasts with the tools to create real-world AI applications.The journey begins with an introduction to LangChain, covering its core concepts, integration with Python, and essential components such as prompt engineering, memory management, and retrieval-augmented generation (RAG). As you progress, you’ll explore advanced AI workflows, including multi-agent architectures, fine-tuning strategies, and optimization techniques to maximize LLM efficiency.The book also takes a deep dive into practical applications of LLMs, guiding you through the development of intelligent chatbots, document retrieval systems, content generation pipelines, and AI-driven automation tools. You’ll learn how to leverage APIs, integrate LLMs into web and mobile platforms, and optimize large-scale deployments while addressing key challenges such as inference latency, cost efficiency, and ethical considerations.By the end of the book, you’ll have gained a solid understanding of LLM architectures, hands-on experience with LangChain, and the expertise to build scalable AI applications that redefine human-computer interaction. What You Will LearnUnderstand the fundamentals of LangChain and Python for LLM developmentKnow advanced AI workflows, including fine-tuning and memory managementBuild AI-powered applications such as chatbots, retrieval systems, and automation toolsKnow deployment strategies and performance optimization for real-world useUse best practices for scalability, security, and responsible AI implementationUnlock the full potential of LLMs and take your AI development skills to the next level Who This Book Is ForSoftware engineers and Python developers interested in learning the foundations of LLMs and building advanced modern LLM applications for various tasks

Harness the power of Large Language Models (LLMs) to build cutting-edge AI applications with Python and LangChain. This book provides a hands-on approach to understanding, implementing, and deploying LLM-powered solutions, equipping developers, data scientists, and AI enthusiasts with the tools to create real-world AI applications.

The journey begins with an introduction to LangChain, covering its core concepts, integration with Python, and essential components such as prompt engineering, memory management, and retrieval-augmented generation (RAG). As you progress, you’ll explore advanced AI workflows, including multi-agent architectures, fine-tuning strategies, and optimization techniques to maximize LLM efficiency.

The book also takes a deep dive into practical applications of LLMs, guiding you through the development of intelligent chatbots, document retrieval systems, content generation pipelines, and AI-driven automation tools. You’ll learn how to leverage APIs, integrate LLMs into web and mobile platforms, and optimize large-scale deployments while addressing key challenges such as inference latency, cost efficiency, and ethical considerations.

By the end of the book, you’ll have gained a solid understanding of LLM architectures, hands-on experience with LangChain, and the expertise to build scalable AI applications that redefine human-computer interaction.

 

What You Will Learn

Understand the fundamentals of LangChain and Python for LLM developmentKnow advanced AI workflows, including fine-tuning and memory managementBuild AI-powered applications such as chatbots, retrieval systems, and automation toolsKnow deployment strategies and performance optimization for real-world useUse best practices for scalability, security, and responsible AI implementationUnlock the full potential of LLMs and take your AI development skills to the next level

 

Who This Book Is For

Software engineers and Python developers interested in learning the foundations of LLMs and building advanced modern LLM applications for various tasks