Bibliography

Table of Contents

Dynamic Programming 🔦

1. (Book) Dynamic Programming and Optimal Control, Volumes 1 and 2, Dimitri Bertsekas. (2020).

2. (Course) Dynamic Programming and Stochastic Control, Prof. Dimitri Bertsekas. (Fall 2015)

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Machine Learning 🎰

1. (Github)100 Days of Machine Learning Coding, Siraj Raval

2. (Book) Machine Learning (Chinese), Zhihua Zhou. (2016).

3. (Course) Machine Learning, Julia Kempe, NYU

4. (Book) Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman, Stanford

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Reinforcement Learning 🌐

Open Resources:

1. (Course) Reinforcement Learning, David Silver, UCL

2. (Book & Related Course Material) Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto

3. (Course) Deep Reinforcement Learning, CS285 at UC Berkeley

4. (Github) A Free course in Deep Reinforcement Learning from beginner to expert

5. (Course) Reinforcement Learning, Bolei Zhou, IERG 5350 at CUHKm

6. (Course) Reinforcement Learning Lecture Series 2021, DeepMind x UCL

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Deep Learning 📖

1. (Book) Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, 2016

2. (Book) Deep Learning with Python, François Chollet, 2017

   (Practice Code)Companion Jupyter notebooks for the book "Deep Learning with Python"

3. (Course) Machine Learning and having it Deep and Structured, Hung-yi Lee, NTU

   (Tutorial) Deep Learning Tutorial: one day's tour, Hung-yi Lee, NTU

4. (Course) CS231n: Convolutional Neural Networks for Visual Recognition, Fei-Fei Li, Stanford

5. (Course) CS224n: Natural Language Processing with Deep Learning, Chris Manning, Stanford

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Combinatorial Optimization 🔦

1. (Course) Discrete Optimization, Prof.Pascal Van Hentenryck, Coursera

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Sustainability 🌎

1. (Document) LIFE CYCLE ASSESSMENT:PRINCIPLES AND PRACTICE,US EPA

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Tools 🔨

1. (Github) How to eat TensorFlow2 in 30 days
2. (Excel) Open-Source RL: Frameworks, Environments, Raw data