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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
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18
Index

Challenges

In this section, we will discuss some of the common challenges associated with deepfake architectures, beginning with a brief discussion of the ethical issues associated with this technology.

Ethical issues

Even though generating fake content is not a new concept, the word deepfake came into the limelight in 2017 when a Reddit user with the name u/deepfakes8 posted fake pornographic videos with celebrity faces superimposed on them using deep learning. The quality of the content and the ease with which the user was able to generate them created a huge uproar on news channels across the globe. Soon, u/deepfakes released an easy-to-set-up application called FakeApp that enabled users to generate such content with very little knowledge of how deep learning works. This led to a number of fake videos and objectionable content. This in turn helped people gain traction on issues associated with identity theft, impersonation, fake news, and so on.

Soon, interest picked...

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