Practical Deep Learning for Coders - Full Course from fast.ai and Jeremy Howard

From the creator

Practical Deep Learning for Coders is a course from fast.ai designed to give you a complete introduction to deep learning. This course was created to make deep learning accessible to as many people as possible. The only prerequisite for this course is that you know how to code (a year of experience is enough), preferably in Python, and that you have at least followed a high school math course. 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 website with questionnaires, set-up guide, and more: https://course.fast.ai/ Lessons 7 and 8 are in a second video: https://youtu.be/HL7LOfyf6bc ⭐️ 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 ⌨️ (https://youtu.be/HL7LOfyf6bc) Lesson 7 - Tabular data ⌨️ (https://youtu.be/HL7LOfyf6bc) Lesson 8 - Natural language processing ⭐️ Book chapters and code on Google Colab ⭐️ 🔗 Full book (or use links below to go directly to a chapter on Google Colab): https://github.com/fastai/fastbook NB: Chapter 2

Published 2020-10-21 · Watch on YouTube ↗
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