Advice, Helpful Links

• 10 Tips for Research and a PhD, Sebastian Ruder

• 23 Research Things

• 50 actionable tips to help you survive your PhD

• How to write a great research paper, Simon Peyton Jones

• How to write a great research proposal, Simon Peyton Jones

• How to give a great research talk, Simon Peyton Jones

• How to write your Ph.D. thesis

• How to write a superb literature review

• How to deal with failure

• How (and How Not) to Write a Good Systems Paper, USENIX

• How to Write and Publish a Research Paper for a Peer-Reviewed Journal

• How to Write a Literature Review Paper

• How to Get My Paper Accepted at Top Software Engineering Conferences

• Networking on the Network: A Guide to Professional Skills for PhD Students

• Being a PhD student shouldn’t be bad for your health, Nature

• Twenty things I wish I’d known when I started my PhD, Nature

• What not to do in graduate school, Nature

• New AIs help researchers to write better, Nature

• Digital tools for researchers

• Courses and Coaching for PhD Students

• Writing Lessons for Engineering and Science

• Guide for graduate students, Kevin Murphy. 

• Don’t miss your PhD deadline

• Writing a scientific article: A step-by-step guide for beginners

• Done is better than perfect: overcoming PhD perfectionism

• Time management for scientists

• The illustrated guide to a Ph.D.

• So long, and thanks for the Ph.D.!

• Tản mạn về mảnh bằng Ph.D (in Vietnamese)

AI, ML, DL Resources

• KDnuggets: Data Mining Community's Top Resource: https://www.kdnuggets.com/ 

• UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/index.php 

• WEKA Machine Learning Project: https://www.cs.waikato.ac.nz/ml/weka/ 

• PyTorch Hub - A pre-trained model repository: https://pytorch.org/hub 

• TensorFlow Hub - A library for reusable machine learning modules: https://www.tensorflow.org/hub 

• Papers With Code - the latest in Machine Learning: https://paperswithcode.com/ 

• Kaggle - Your Home for Data Science: https://www.kaggle.com/

• Kaggle Winning Solutions: http://kagglesolutions.com/ 

• Profillic: AI research & source code to supercharge your projects: https://www.profillic.com/ 

• Machinelearningcoban SOTA - State of the art in Machine Learning: https://forum.machinelearningcoban.com/c/sota 

• MIT ML Resources: https://sgfin.github.io/learning-resources/ 

• Google AI: https://ai.google/ 

• Google AI Blog: https://ai.googleblog.com/ 

• Google Dataset Search: https://toolbox.google.com/datasetsearch 

• Google Colab: https://colab.research.google.com/ 

• Microsoft Research: https://www.microsoft.com/en-us/research/ 

• Microsoft Research Open Data: https://msropendata.com/ 

• Facebook AI: https://ai.facebook.com/ 

• Facebook Research: https://research.fb.com/ 

• Facebook Code: https://code.fb.com/ 

• NVIDIA Research: https://www.nvidia.com/en-us/research/ 

• IBM Research: https://www.ibm.com/blogs/research/ 

• IBM Community: https://www.ibm.com/community/ 

• Arxiv - Computer Science Recent Submissions: https://arxiv.org/list/cs/recent 

• ML News: https://groups.google.com/forum/#!forum/ml-news 

• Data Science Central: https://www.datasciencecentral.com/ 

• Machine Learning Course: https://tec4tric.com/2018/11/machine-learning-course.html 

• Machine Learning Books: https://tec4tric.com/2018/07/machine-learning-books.html 

• Montreal-AI-Academy: https://montrealartificialintelligence.com/academy/ 

• Machine Learning for Everyone: https://vas3k.com/blog/machine_learning/ 

• Fast.ai: https://www.fast.ai/ 

• Vinod Sharma's Blog: https://vinodsblog.com/ 

• FloydHub Blog: https://blog.floydhub.com/ 

• Lil'Log: https://lilianweng.github.io/lil-log/ 

• Towards AI: https://towardsai.net/ 

• Towards Data Science: https://towardsdatascience.com/ 

• Deep Learning Drizzle: https://deep-learning-drizzle.github.io/ 

• Awesome-DeepLearning-Resources: https://github.com/endymecy/awesome-deeplearning-resources 

• Dive into Deep Learning: http://d2l.ai/ ; https://github.com/dsgiitr/d2l-pytorch 

• http://machineslearner.com/blog/ 

• https://explained.ai/

• https://blog.paperspace.com/ 

• ML in Vietnamese: https://machinelearningcoban.com/; https://ongxuanhong.wordpress.com; https://nttuan8.com/gioi-thieu-ve-deep-learning/; http://ainoodle.tech/ ; https://ai.hblab.vn/ 

Chung-Ang University

• Homepage: https://neweng.cau.ac.kr/index.do 

• Graduate School: http://graduate.cau.ac.kr/ 

• Office of International Affairs: https://oia.cau.ac.kr 

• School of CSE: https://cse.cau.ac.kr/main.php 

• KE Lab: http://ke.cau.ac.kr 

• Research Guide CAU: https://researchguide.cau.ac.kr/ 

• Library CAU: https://library.cau.ac.kr/en/

• dCollection CAU: http://dcollection.cau.ac.kr/

• CAU Portal: https://mportal.cau.ac.kr ; http://sugang.cau.ac.kr 

• CAU News: https://news.cau.ac.kr/index.do 

• Copy Killer CAU: https://caugrad.copykiller.com/ 

• CAU Community: https://cauin.cau.ac.kr/ 

• Survival Guide at CAU (and in Korea): https://cauexchange.blog/category/survival-guide/ 

SeoulTech

• Homepage: https://en.seoultech.ac.kr/, https://www.seoultech.ac.kr/ 

• ICE Lab: https://www.icelab.re.kr/ 

Computer Network Research

• IEEE Future Networks: https://futurenetworks.ieee.org/, https://futurenetworks.ieee.org/conferences

• Top Computer Science Conferences for Computer Networks and Communications: https://research.com/conference-rankings/computer-science/network-communication

• Machine Learning For Communications Emerging Technologies Initiative: https://mlc.committees.comsoc.org/

• Best Readings: https://www.comsoc.org/publications/best-readings