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- Machine Learning/Deep Learning (This year I need to go deeper)
- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- more theory and fundamental knowledge
- 动手学深度学习 (Dive into Deep Learning) by Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola
- Focus on code and implementation, both MXNet and Pytorch
- 机器学习 by 周志华
- 统计学习方法 by 李航
- Did not realize that this Chinese book affected lots of ML researchers
- 终极算法 (The Master Algorithm) by Pedro Domingos
- Still do not know what this book is talking about, whether it is more of history or future
- (ML) System
- Productivity / Business & Economics / Time Management
- Getting Things Done (1 to 3) by David Allen
- Need to read this every year
