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Generative AI with Python and PyTorch

Generative AI with Python and PyTorch

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

Generative AI with Python and PyTorch

By: Joseph Babcock, Raghav Bali

Overview of this book

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. You’ll 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 you’re 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.
Table of Contents (3 chapters)
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Summary

In this chapter, we've covered the basic vocabulary of deep learning—how initial research into perceptrons and MLPs led to simple learning rules being abandoned for backpropagation. We also looked at specialized neural network architectures such as CNNs, based on the visual cortex, and recurrent networks, specialized for sequence modeling. Finally, we examined variants of the gradient descent algorithm proposed originally for backpropagation, which have advantages such as momentum, and described weight initialization schemes that place the parameters of the network in a range that is easier to navigate to a local minimum.

With this context in place, we are all set to dive into projects in generative modeling, beginning with the generation of MNIST digits using Deep Belief Networks in Chapter 11, Painting Pictures with Neural Networks Using VAEs.

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