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Generative AI with Python and PyTorch

You're reading from Generative AI with Python and PyTorch Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications

Product type Paperback

Published in Mar 2025

Publisher Packt

ISBN-13 9781835884447

Length 450 pages

Edition 2nd Edition

Languages

Python

Tools

PyTorch

Concepts

Artificial Intelligence

Authors (2):

Joseph Babcock

Raghav Bali

View More author details

Table of Contents (18) Chapters

Preface

1. Introduction to Generative AI: Drawing Data from Models

2. Building Blocks of Deep Neural Networks FREE CHAPTER

3. The Rise of Methods for Text Generation

4. NLP 2.0: Using Transformers to Generate Text

5. LLM Foundations

6. Open-Source LLMs

7. Prompt Engineering

8. LLM Toolbox

9. LLM Optimization Techniques

10. Emerging Applications in Generative AI

11. Neural Networks Using VAEs

12. Image Generation with GANs

13. Style Transfer with GANs

14. Deepfakes with GANs

15. Diffusion Models and AI Art

16. Other Books You May Enjoy

17. Index

Emerging trends and research areas

We have covered quite a bit of ground in this chapter so far; before we close, let us quickly touch upon a few emerging trends specifically aimed toward bringing improvements and efficiencies.

Alternate architectures

Earlier in the chapter, we covered a number of variations of the transformer architecture that make use of different tricks and techniques to bring in efficiencies. Mamba^{32, 33} and RWKV³⁴ are two alternate architectures developed from the ground up and are aimed at solving bottlenecks with transformer architectures while maintaining their immensely powerful characteristics.

Mamba is a Selective State Space Model (SSM or S4) that improves over transformer architectures while scaling linearly in sequence length. SSMs are designed to selectively identify and focus on the most relevant parts of the input sequence as compared to transformers and traditional SSMs that process all inputs uniformly. They combine the best elements...

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Authors (2)

Joseph Babcock

Joseph Babcock

Joseph Babcock has spent over a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Throughout his career, he has worked on recommender systems, petabyte-scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to drug discovery and genomics.

See other products by Joseph Babcock

Raghav Bali

Raghav Bali

Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.

See other products by Raghav Bali

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