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Total Size:
13.0 MB
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961ECB713A5EAEF8E50AE46851A08FC5320026EE
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Aug. 28, 2025, 12:31 p.m.
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(Last updated: Sept. 20, 2025, 1:33 a.m.)
| File | Size |
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| Bhati D. A Beginner's Guide to Generative AI. An Introductory Path...2026.pdf | 13.0 MB |
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35.0 MB
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2024-12-30
| Uploaded by indexFroggy | Size 35.0 MB | Health [ 20 /7 ] | Added 2024-12-30 |
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SOURCE: Bhati D. A Beginner's Guide to Generative AI. An Introductory Path...2026
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COVER
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MEDIAINFO
Textbook in PDF format This book is the essential guide for anyone curious about AI’s creative power. In the rapidly evolving landscape of Artificial Intelligence (AI), Generative AI stands out as one of the most transformative technologies of our time. Designed for beginners and requiring no prior knowledge of AI, this book breaks down the fundamentals of generative AI, from text and image generation to the workings of models like ChatGPT and Google Bard. The authors provide step-by-step coverage of the essential concepts and techniques that power generative AI. From the basics of how machines learn to generate text and images, to the intricate workings of models like Transformers, ChatGPT, and Google Bard, readers will gain a solid foundation in AI's most cutting-edge tools. Rather than focusing on a single method, the authors introduce a spectrum of generative modeling techniques, including diffusion models, variational autoencoders, and transformers. This comprehensive exposure ensures readers will be well-prepared to understand and adapt to the rapidly evolving AI landscape. In addition, real-world applications of generative AI across various industries are explored including healthcare innovations, business analytics, and legal technology, and the authors provide practical insights and examples that show how Generative AI is revolutionizing these fields. The Transformer Architecture is an improvement in NN models, particularly in capturing sequential information. Before Transformers, Recurrent Neural Networks (RNNs) and their more evolutionarily developed counterpart, Long Short-Term Memory (LSTM) networks, used to be the go-to architectures for sequential data processing, like text or time series. RNNs and LSTMs also helped but Transformers brought certain changes that eliminated their drawbacks and added significant enhancements performance and effectiveness. Understand the practical part of setting up the environment with Python to work with Large Language Models (LLMs) and see how text generation with these models can be done. For this, we will use Hugging Face Transformers, which is an open-source framework that supports pre-trained models such as GPT-2, GPT-3, and others. Preface Introduction to Generative AI Evolution of Neural Networks to Large Language Models LLMs and Transformers The ChatGPT Architecture: An In-Depth Exploration of OpenAIs Google Bard and Beyond. Diffusion Model and Generative AI for Images Setting Up the Environment and Implementing LLMs ChatGPT Use Cases
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