The repository consists of quality and must-know machine learning papers.
Topics are arranged in categorical natured way. ML101 have extra insights
on topics lately discovered and is not hyped yet.
On deep networks for vision and NLP tasks
Notes extracted from AndrewNG deeplearning teachings
Papers on generic topics that helped ML to achieve what it is today
Mathematical topics essential for ML understanding
Summarization of 101 papers on computer vision related model architecture
On RL
General supervised ML papers
On unsupervised ML papers