9781835884454_

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 Description

Become 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. Youll 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 youre 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 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.

...(*_*)9781835884454_

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

...(*_*)9781835884454_

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

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 for

...(*_*)9781835884454_

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 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

Generative AI with Python and PyTorch

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