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Generative AI with Python and PyTorch

Generative AI with Python and PyTorch - Second Edition

By : Joseph Babcock, Raghav Bali
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Generative AI with Python and PyTorch

Generative AI with Python and PyTorch

5 (1)
By: Joseph Babcock, Raghav Bali

Overview of this book

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.
Table of Contents (19 chapters)
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17
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Index

Creating complex applications with LangGraph

Now we’ve made a basic translation application, where a user provides an answer to a templated prompt and the LLM provides a translation. For our next example, we’re going to build on this framework in a few key ways by designing a question-answering application that chains together several important capabilities:

  • We will enable open-ended dialogue through a chatbot
  • We’ll use a vector database to retrieve relevant documents to our query from an internal store
  • We’ll add a memory that allows the bot to keep track of its interactions with us
  • We’ll provide the ability for feedback from a human-in-the-loop user
  • We’ll provide the ability to look on the internet for additional content in response to prompts

By doing so, we’ll move from specifying a chain, where commands are processed in a linear order, to graphs where LLM outputs are used to determine...

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