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
- 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
Joseph Babcock has spent over a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Throughout his career, he has worked on recommender systems, petabyte-scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to drug discovery and genomics.
Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.
