خرید و دانلود نسخه کامل کتاب Mastering Transformer Architecture with Python: From Attention Mechanisms to Production Deployment (Python Series – Learn. Build. Master. Book 13)

آنتونی رابینز میگه : من در 40 سالگی به جایی رسیدم که برای رسیدن بهش 82 سال زمان لازمه و این رو مدیون کتاب خواندن زیاد هستم.

 
Transformers power every major AI breakthrough today. From ChatGPT and GPT-4 to LLaMA, BERT, Stable Diffusion, and Whisper, the transformer architecture is the engine behind modern artificial intelligence. If you want to build, customize, or deploy AI systems, understanding transformers is no longer optional. It is essential. This book teaches you transformer architecture from the inside out. You will not just learn how transformers work. You will build them yourself, component by component, in Python and PyTorch.What You Will Build: Starting with the attention mechanism, you will implement scaled dot-product attention and multi-head attention from scratch. You will understand why scaling by the square root of d_k matters, how causal masking enables autoregressive generation, and how different attention heads specialize in capturing different linguistic relationships. Every concept is explained with clear theory, intuitive illustrations, and runnable code.From there, you will build complete transformer models. You will assemble encoder blocks and decoder blocks with feed-forward networks, layer normalization, and residual connections. You will connect them into a full encoder-decoder transformer and train it to translate English to French. Then you will explore the three major architecture families that dominate modern AI: encoder-only models like BERT for text understanding, decoder-only models like GPT and LLaMA for text generation, and encoder-decoder models like T5 and BART for summarization and translation.Beyond Text — Vision, Speech, and Multimodal AI: This book goes far beyond NLP. You will learn how Vision Transformers (ViT) process images as sequences of patches, how CLIP connects vision and language for zero-shot classification, and how Whisper applies transformers to speech recognition across 99 languages. You will see that the transformer is not just a language model — it is a universal sequence processing engine.Production Engineering From Training to Deployment: The practical chapters cover everything you need for real-world applications. You will master Flash Attention, sparse attention patterns, Mixture of Experts, and model quantization (INT8 and INT4). You will learn mixed-precision training, gradient accumulation, distributed training across multiple GPUs, and how to diagnose common training failures. Finally, you will deploy models in production using ONNX, vLLM, and FastAPI, with monitoring, batching strategies, and cost optimization.The Cutting Edge: The final chapter surveys the frontier Mamba and state space models, hybrid architectures, retrieval-augmented generation (RAG), RLHF alignment, agentic AI, and long-context transformers supporting over one million tokens.Why This Book? Every concept is built from scratch before using libraries. You will understand the why behind every design decision, not just the what. All code runs on Google Colab with free GPU access. The progression from theory to implementation to production gives you the complete picture that tutorials and blog posts cannot provide.Whether you are a data scientist, ML engineer, software developer, or AI researcher, this book gives you the deep, practical understanding of transformer architecture that the AI industry demands.Book 13 in the Python Series Learn. Build. Master.