Master GenAI 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
• Fine-tune models with PEFT and LoRA to speed up training
• Expand 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 Description

Become an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable.

From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence.

You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models.

Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI.

What you will learn

• Grasp 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
• Understand how attention and transformers have changed NLP
• Optimize your diffusion models by combining them with VAEs
• Build text generation pipelines based on LSTMs and LLMs
• Leverage the power of open-source LLMs, such as Llama and Mistral, for diverse applications

Who this book is for

This 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

1. Introduction to Generative AI: Drawing Data from Models
2. Building Blocks of Deep Neural Networks
3. The Rise of Methods for Text Generation
4. NLP 2.0: Using Transformers to Generate Text
5. LLM Foundations
6. Open-Source LLMs
7. Prompt Engineering
8. LLM Toolbox
9. LLM Optimization Techniques
10. Emerging Applications in Generative AI
11. Neural Networks Using VAEs
12. Image Generation with GANs
13. Style Transfer with GANs
14. Deepfakes with GANs
15. Diffusion Models and AI Art