Useful Links

Paper Reading and Scientific Writing

1. How to Read a Paper, S. Keshav, http://ccr.sigcomm.org/online/files/p83-keshavA.pdf 

2. "The Elements of Style", William Strunk Jr., https://www.amazon.com/Elements-Style-Fourth-William-Strunk/dp/020530902X

3. "How to write papers so people can read them," Derek Dreyer, https://people.mpi-sws.org/~dreyer/talks/talk-plmw20icfp.pdf 

4. "Mathematical Writing", Donald E. Knuth, Tracy Larrabee, and Paul M. Roberts, https://tex.loria.fr/typographie/mathwriting.pdf 

5. "A few tips on writing papers with mathematical content", John Tsitsiklis, https://www.mit.edu/~jnt/Papers/R-19-writing-v4.pdf 

6. "A Guide to Writing Mathematics," Kevin P. Lee, https://web.cs.ucdavis.edu/~amenta/w10/writingman.pdf 

7. "How to write a technical paper," Michael Ernst, https://homes.cs.washington.edu/~mernst/advice/write-technical-paper.html 

8. "Learn LaTeX in 30 minutes," Overleaf, https://www.overleaf.com/learn/latex/Learn_LaTeX_in_30_minutes

9. "10 tips for academic talks," Matt Might, https://matt.might.net/articles/academic-presentation-tips/ 

10. "Research 101 Paper Writing with LaTeX," Jia-Bin Huang, https://gangw.cs.illinois.edu/latex.pdf 

11. LaTeX Usage Notes, Eddie Kohler, https://www.read.seas.harvard.edu/~kohler/latex.html 

Ph.D. Life

1. "The Ph.D. Grind, A Ph.D. Student Memoir,"  Philip Guo, http://pgbovine.net/PhD-memoir/pguo-PhD-grind.pdf 

2. CS PhD --help, https://phdadvice.carrd.co/, (a collection of great advice)

Machine Learning Courses and Tools

Basics

1. Coursera Machine Learning (Andrew Ng),  https://www.coursera.org/course/ml

2. Coursera Deep Learning (Andrew Ng), https://www.coursera.org/specializations/deep-learning

CV

1. CS231n: Convolutional Neural Networks for Visual Recognition (Fei-fei Li), http://cs231n.stanford.edu 

NLP

1. CS224n: Natural Language Processing with Deep Learning (Chris Manning), http://web.stanford.edu/class/cs224n/

2. CS224d: Deep Learning for Natural Language Processing (Richard Socher), http://cs224d.stanford.edu

Graph

1. CS224W: Machine Learning with Graphs (Jure Leskovec), http://web.stanford.edu/class/cs224w/index.html

RL

1. Deep Reinforcement Learning (Sergey Levine), http://rail.eecs.berkeley.edu/deeprlcourse/

2. Advanced Topics: RL (David Silver), http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

3. DRL Policy Optimization (Hungyi Lee in Chinese), https://www.youtube.com/watch?v=z95ZYgPgXOY&list=PLJV_el3uVTsODxQFgzMzPLa16h6B8kWM_

UL

1. Deep Unsupervised Learning (Pieter Abbeel), https://sites.google.com/view/berkeley-cs294-158-sp20/home?authuser=0

DL Stack

1. Full Stack Deep Learning, https://fullstackdeeplearning.com/ 

Tools

1. The Missing Semester of Your CS Education, https://missing.csail.mit.edu/ 

2. Python Virtual Environment: Anaconda, https://www.anaconda.com

3. Python IDE: Pycharm, Visual Studio Code

4. Python Interactive Environment: Jupyter Lab, https://jupyterlab.readthedocs.io/en/stable/

5. SSH session management tools: 

   1. Byobu, https://www.byobu.org/home

   2. screen, https://www.digitalocean.com/community/tutorials/how-to-install-and-use-screen-on-an-ubuntu-cloud-server

6. Machine Learning framework: scikit-learn, https://scikit-learn.org/

7. Deep Learning framework: 

   1. pytorch, https://pytorch.org/

   2. tensorflow, https://www.tensorflow.org/

   3. mxnet, mxnet.apache.org