Summary of Deep Learning and Machine Learning — Python Data Structures and Mathematics Fundamental: From Theory to Practice, by Silin Chen and Ziqian Bi and Junyu Liu and Benji Peng and Sen Zhang and Xuanhe Pan and Jiawei Xu and Jinlang Wang and Keyu Chen and Caitlyn Heqi Yin and Pohsun Feng and Yizhu Wen and Tianyang Wang and Ming Li and Jintao Ren and Qian Niu and Ming Liu
Deep Learning and Machine Learning – Python Data Structures and Mathematics Fundamental: From Theory to Practice
by Silin Chen, Ziqian Bi, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Keyu Chen, Caitlyn Heqi Yin, Pohsun Feng, Yizhu Wen, Tianyang Wang, Ming Li, Jintao Ren, Qian Niu, Ming Liu
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Programming Languages (cs.PL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This book provides a comprehensive introduction to machine learning (ML) and deep learning (DL), bridging the gap between theoretical mathematics and practical application. The book focuses on Python as the primary programming language for implementing key algorithms and data structures. It covers basic and advanced Python programming, mathematical operations, matrix operations, linear algebra, optimization techniques, neural networks, optimization algorithms, frequency domain methods, and real-world applications of large language models (LLMs) and artificial intelligence (AI) in big data management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This book is about teaching machine learning and deep learning using Python. It starts with the basics and goes all the way to advanced topics like neural networks and AI. The book shows how math is used to develop AI solutions that can handle big data. There are examples and code throughout, so you can practice what you learn. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Optimization
