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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

Text generation with GPT

OpenAI has been in the spotlight for quite some time because of its newsworthy works, such as GPT8, GPT-29, and 3 (and also instructGPT, 3.5, and 4, along with viral sensation ChatGPT, but these are a bit different and covered in subsequent chapters). In this section, we will briefly discuss GPT architectures up to GPT-3. We will then use a pretrained version of GPT-2 for our text generation task.

Generative re-training: GPT

The first model in this series is called GPT, or Generative Pretraining. It was released in 2018, about the same time as BERT. The paper presents a task-agnostic architecture based on the ideas of transformers and unsupervised learning. The GPT model was shown to beat several benchmarks, such as GLUE and SST-216, although its performance was overtaken by BERT, which was released shortly after this.

GPT is essentially a language model based on the transformer-decoder we presented previously. Since a language model can be trained...

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