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Full Description
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 DescriptionBecome 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 and capabilities of LLMs
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 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.
Contents
Table of Contents
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
LLM Foundations
Open-Source LLMs
Prompt Engineering
LLM Toolbox
LLM Optimization 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
