Deep learning resources

There is no better era to self-teach deep learning! Besides the well-known resource platforms such as Kaggle, the machine learning roadmap, I recommend several resources that I found really amazing and the course sequences to follow. The very initial start is still the courses offered on Coursera, or the Standford cn231n (see below the item 4) on Youtube, great accelerators!

  1. A dive into deep learning “Interactive deep learning book with code, math, and discussions.” This learning material is classi! I got to know this late but anyone can benefit from it at any stage. All the scripts can be ran in google colab. The interpretation is amazing. The Chinese version of it is the top1 seller in the Chinese bookstore market. The Chinese version is great, read as originates from Chinese authors, not as many books with very rough translation. Many university used it for classes already and an AWS space can be applied for free for teaching purposes. This book, interactive as it suggests, may be a better start compared to the two classical deep learning books, namely DEEP LEARNING with PYTHON and DEEP LEARNING, as it is up-to-date, very practical with real-life scripts, and enables discussions.
  2. A fantastic online journal with great visualisations.
  3. Paper and code as the names suggests, this is the great trend pushing by the field of machine learning. In the same vein is the OpenReview.
  4. Courses on Youtube: The sequence to watch I recommend is (1) standford cn231 (the winter or summer semester), which is the most detailed and classical course; (2) MIT 6.S191 which is quite a good introduction of the deep learning realms, less detailed; (3) Unsupervised deep learning by Pieter Abbeel at UC Berkeley for people interested in deep learning or would like to dive deeper. (4) DeepMind x UCL | Deep Learning Lectures, which is more fast-space and advanced, and let the audience glimpse into the newest developments till 2020.
  5. For people who can read Chinese, the CSDN for numerous insightful blogs and resources. The CSDN has been around for ages, but I just got to know it, the articles published deepens my understandings greatly!! I am inspired by the enthusiasm of the community.
  6. Maybe needless to mention, following people’s researchgate, github, twitter, linkins, subscribe to the youtube channels so that you will always be updated.

I will keep updating the list, enjoy learning!

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