PyTorch for Modern Generative AI Building LLMs, Diffusion Systems, and Agents from the Ground Up [#1019763]

PyTorch for Modern Generative AI: Building LLMs, Diffusion Systems, and Agents from the Ground Up
English | 3 Jan. 2026 | ASIN: B0GDTVP33G | 355 pages | Epub | 759.82 KB
Master the Frontiers of AI with PyTorch Unlock the power of Generative AI and build the next generation of intelligent systems-from Large Language Models to cutting-edge Diffusion Models. The AI landscape is shifting rapidly. To stay ahead, you need more than just a surface-level understanding of libraries; you need to master the architecture, the math, and the implementation. PyTorch for Modern Generative AI is your comprehensive, hands-on guide to building state-of-the-art models from scratch. Whether you are a data scientist, a software engineer, or an AI enthusiast, this book bridges the gap between theoretical research and production-ready code. What You Will Build and Master: Large Language Models (LLMs): Go beyond the basics of Transformers. Learn to build, fine-tune, and optimize models like GPT and Llama using advanced techniques like LoRA and QLoRA. Diffusion Systems: Deconstruct the magic behind Stable Diffusion and Midjourney. Implement forward and reverse diffusion processes to generate high-fidelity images. Autonomous Agents: Shift from static models to dynamic agents. Learn how to implement Tool-Use, ReAct patterns, and multi-agent orchestration. Production-Grade PyTorch: Master the ecosystem including PyTorch Lightning, TorchServe, and distributed training across multiple GPUs. Optimization & Scaling: Implement Quantization, FlashAttention, and KV-caching to make your models faster and more efficient. Why Choose This Book? Code-First Approach: Every chapter features clean, modular PyTorch code that you can adapt for your own projects. Up-to-Date Architectures: Covers the latest breakthroughs in 2024 and 2025 AI research. No "Black Boxes": We explain the why behind the how , ensuring you understand the underlying calculus and probability of generative systems. Real-World Applications: Move from Jupyter notebooks to scalable systems with chapters dedicated to deployment and monitoring. Inside the Book: Foundations: Advanced PyTorch tensors, autograd deep-dive, and custom layers. The Transformer Revolution: Building attention mechanisms and positional encodings from the ground up. Generative Adversarial Networks (GANs) vs. VAEs: Understanding the evolution of generative modeling. Deep Dive into Diffusion: U-Nets, Schedulers, and Latent Spaces. LLM Engineering: Prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning. The Future of Agents: Memory, planning, and executing complex tasks. Stop consuming AI-start building it. Equip yourself with the skills to lead the generative revolution. Scroll up and click "Buy Now" to start building the future today!