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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
Other Books You May Enjoy
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Index

Multilayer perceptrons and backpropagation

While large research funding for neural networks declined until the 1980s after the publication of Perceptrons, researchers still recognized that these models had value, particularly when assembled into multilayer networks, each composed of several perceptron units. Indeed, when the mathematical form of the output function (that is, the output of the model) was relaxed to take on many forms (such as a linear function or a sigmoid), these networks could solve both regression and classification problems, with theoretical results showing that three-layer networks could effectively approximate any output.13 However, none of this work addressed the practical limitations of computing the solutions to these models, with rules such as the perceptron learning algorithm described earlier proving a great limitation to their applied use. A central problem was how to appropriately estimate the weights in the hidden layers of the network, which form the...

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