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Data Science for Web3

You're reading from   Data Science for Web3 A comprehensive guide to decoding blockchain data with data analysis basics and machine learning cases

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781837637546
Length 344 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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Gabriela Castillo Areco Gabriela Castillo Areco
Author Profile Icon Gabriela Castillo Areco
Gabriela Castillo Areco
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Toc

Table of Contents (23) Chapters Close

Preface 1. Part 1 Web3 Data Analysis Basics
2. Chapter 1: Where Data and Web3 Meet FREE CHAPTER 3. Chapter 2: Working with On-Chain Data 4. Chapter 3: Working with Off-Chain Data 5. Chapter 4: Exploring the Digital Uniqueness of NFTs – Games, Art, and Identity 6. Chapter 5: Exploring Analytics on DeFi 7. Part 2 Web3 Machine Learning Cases
8. Chapter 6: Preparing and Exploring Our Data 9. Chapter 7: A Primer on Machine Learning and Deep Learning 10. Chapter 8: Sentiment Analysis – NLP and Crypto News 11. Chapter 9: Generative Art for NFTs 12. Chapter 10: A Primer on Security and Fraud Detection 13. Chapter 11: Price Prediction with Time Series 14. Chapter 12: Marketing Discovery with Graphs 15. Part 3 Appendix
16. Chapter 13: Building Experience with Crypto Data – BUIDL 17. Chapter 14: Interviews with Web3 Data Leaders 18. Index 19. Other Books You May Enjoy Appendix 1
1. Appendix 2
2. Appendix 3

Summary

Throughout this chapter, we explored three different approaches to using AI tools in artistic projects. We examined the use of GAN models for colorizing sketches, explored the VGG19 model for transferring style, and discovered state-of-the-art applications of Stable Diffusion models for generating art based on prompts.

Moreover, we learned about the entire workflow, from the finished piece of art to listing the final image on a marketplace. By combining the power of AI and blockchain technology, we now have a range of new opportunities to explore and monetize artistic work in exciting and innovative ways.

It is worth noting that questions have arisen concerning the ownership of images generated using AI. This arises from the possibility that these models may have been trained on copyrighted pieces of art without the artist’s consent. Respondents contend that the transformative nature of the model’s outputs, coupled with the fair use argument, could potentially...

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