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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

Dolly

LLaMA3 and Mixtral-8x7B are both trained on huge amounts of web data. The next open model we’ll examine, “Dolly,” was created by the company DataBricks to illustrate the power of fine-tuning with smaller datasets. The original version of the Dolly model was created by DataBricks to illustrate how the instruction-following abilities of ChatGPT described in the InstructGPT paper12 can be replicated in smaller models using high-quality datasets.

Instruction-following models are created through additional training on LLMs following the initial training, which focuses on predicting the next token in a prompt given a context window of input text. The textual output generated by this next-token predictor is not well-suited for complex tasks such as brainstorming ideas, summarizing content, or question and answer, nor does it have the toxicity and safety filters needed for commercial use.

Thus, these first-stage models are further refined using Reinforcement...

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