Generative AI with Python and PyTorch - Second Edition: Navigating the AI frontier with LLMs, Stable Diffusion, and next¿gen AI applications, Generative AI with Python and PyTorch - Second Edition: Navigating the AI frontier with LLMs, Stable Diffusion, and next¿gen AI applications

Master generative AI techniques to create images and text using Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), LSTMs, and Large Language Models (LLMs) Key Features Implement real-world applications of LLMs and generative AI Use PEFT and LoRA to fine-tune models with a subset of the model weights to speed up training Enhance your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndex Purchase of the print or Kindle book includes a free eBook in PDF format Book DescriptionBecome an expert in generative AI through practical projects to leverage cutting-edge models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch, Second Edition, is your comprehensive guide to creating advanced AI applications. Leveraging Python, this book provides a detailed exploration of the latest generative AI technologies.From NLP to image generation, this edition dives into practical applications and the underlying theories that enable these technologies. By integrating the latest advancements and applications of large language models, this book prepares you to design and implement powerful AI systems that transform data into actionable insights.You’ll build your LLM toolbox by learning about various models, tools, and techniques, including GPT-4, LangChain, RLHF, LoRA, and retrieval augmented generation. This deep learning book shows you how to generate images and apply styler transfer using GANs, before implementing CLIP and diffusion models.Whether you’re creating dynamic content or developing complex AI-driven solutions, Generative AI with Python and PyTorch, Second Edition, equips you with the knowledge to use Python and AI to their full potential. What you will learn Understand the core concepts behind large language models and their capabilities Craft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputs Learn how attention and transformers have changed NLP Optimize your diffusion models by combining them with VAEs Build several text generation pipelines based on LSTMs and LLMs Leverage the power of open-source LLMs, such as Llama and Mistral, for various tasks Who this book is forThis book is for data scientists, machine learning engineers, and software developers seeking practical skills in building generative AI systems. A basic understanding of math and statistics and experience with Python coding is required. Table of Contents An 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 Foundations of LLMs Open Source LLMs Prompt Engineering LLM Toolbox/Ecosystem LLM Optimisation 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