Deep Learning Guide
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Contents
First Steps[edit | edit source]
Start with Machine Learning course on Coursera.
Then you should move on to CS231n- course by Stanford, the notes on Github are intuitive. There is also another course by Stanford on Natural Language Processing with Deep Learning. This course released the public videos for the latest edition of the course offered in Winter'19, updating with the latest advancements in Natural Language Processing.
Another path recently followed by deep learning enthusiasts is the Deep Learning specialization on Coursera. The specialization contains 5 courses with proper case studies. There is also a Udacity course developed with Google. You can select any of the above courses as per your schedule.
After getting confident enough which you should try reading research papers, and implement some of them. One good resource for a collection of the most influential research paper is Deep Learning Roadmap.
And try participating in Kaggle competitions.
Which tools[edit | edit source]
You should be comfortable to convert any research paper to code. The community generally uses PyTorch for most research purpose, but Keras is easier to begin with. Other options are:
How to get help?[edit | edit source]
- Deep Learning - Reddit
- Machine Learning - Reddit
- StackOverflow
- Awesome deep learning- A curated list of deep learning projects, tutorials, research papers and much more!
List of Interesting Blogs and Communities[edit | edit source]
- Deep Learning enthusiasts on Twitter
- Andrej Karpathy's blog
- Christopher Olah's blog
- Michael A. Nielsen, "Neural Networks and Deep Learning", Determination Press, 2015
- Denny Britz's blog
- deeplearning.net
- Yoshua Bengio's AMA
- Yann LeCun'd AMA
- OpenAI Team's AMA
- Adit Deshpande's blog
