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Practical Deep Learning for Coders - Full Course from fast.ai and Jeremy Howard
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This course was developed by Jeremy Howard and Sylvain Gugger. Jeremy has been using and teaching machine learning for around 30 years. He is the former president of Kaggle, the world's largest machine learning community. Sylvain Gugger is a researcher who has written 10 math textbooks.
⭐️ Course Contents ⭐️
(See next section for book & code.)
⌨️ (0:00:00) Lesson 1 - Your first modules
⌨️ (1:22:55) Lesson 2 - Evidence and p values
⌨️ (2:53:59) Lesson 3 - Production and Deployment
⌨️ (5:00:20) Lesson 4 - Stochastic Gradient Descent (SGD) from scratch
⌨️ (7:01:56) Lesson 5 - Data ethics
⌨️ (9:09:46) Lesson 6 - Collaborative filtering
⭐️ Book chapters and code on Google Colab ⭐️
NB: Chapter 2 contains widgets, which unfortunately are not supported by Colab. Also, in some places we use a file upload button, which is also not supported by Colab. For those sections, either skip them, or use a different platform such as Gradient (Colab is the only platform which doesn't support widgets).
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