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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
18
Index

Challenges

GANs provide an alternative method of developing generative models. Their design inherently helps in mitigating issues we discussed with some of the other techniques. However, GANs are not free from their own set of issues. The choice of developing models using concepts of game theory is fascinating yet difficult to control. We have two agents/models trying to optimize opposing objectives, which can lead to all sorts of issues. Some of the most common challenges associated with GANs are as follows.

Training instability

GANs play a minimax game with opposing objectives. No wonder this leads to oscillating losses for generator and discriminator models across batches. A GAN setup that is training well will typically have higher variation in losses initially, but eventually, it stabilizes, and so does the loss of the two competing models. Yet it is very common for GANs (especially vanilla GANs) to spiral out of control. It is difficult to determine when to stop the...

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