Deep Learning Resources
- Deep Learning by Goodfellow, Bengion, Courville is as complete a textbook as can be, for such a fast-growing field. It spends some time on machine learning, and quickly dives into CNN, RNN and deep generative models. I loved the emphasis on DL algorithms but with minimal coding. That Elon Musk and Geoff Hinton recommend this book already speaks volumes!
- Alex Graves' book on "Supervised Sequence Labelling with Recurrent Neural Networks" is a great place to learn about RNNs and LSTMs from. It is freely available as a pre-print on the web.
- Reinforcement Learning: An Introduction is again a freely-available resource. Also see Hugo Larochelle's course.
Online Courses
- MIT 6.S191
Introduction to Deep Learning is a very modern of vid lectures from MIT, delivered by Alex Amini and Ava Soleimany. While it still has a classroom style-format, the course incorporates lots of cool demos, live coding and amazing results. I loved going through a previous version, and which I thought very highly of. The MIT Deep Learning lecture series should also be followed in parallel.
- DataCamp has lots of wonderful mini-series (~4-10 hrs each) that teach Deep Learning skillsets, like TensorFlow, Keras, etc.
- Geoff Hinton's webpage, has lots and lots of reading materials, links to even more resources and tutorials. A fair warning would be that some of the stuff mentioned here is rather old, unless one is interested in the early days (2000s!) of deep learning.
