Practical Deep Learning for Coders – Full Course from and Jeremy Howard

Practical Deep Learning for Coders is a course from 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:

Lessons 7 and 8 are in a second video:

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
( Lesson 7 – Tabular data
( 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):

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).

Intro to Jupyter:
Chapter 1, Intro:
Chapter 2, Production:
Chapter 3, Ethics:
Chapter 4, MNIST Basics:
Chapter 5, Pet Breeds:
Chapter 6, Multi-Category:
Chapter 7, Sizing and TTA:
Chapter 8, Collab:
Chapter 9, Tabular:
Chapter 10, NLP:
Chapter 11, Mid-Level API:
Chapter 12, NLP Deep-Dive:
Chapter 13, Convolutions:
Chapter 14, Resnet:
Chapter 15, Arch Details:
Chapter 16, Optimizers and Callbacks:
Chapter 17, Foundations:
Chapter 18, GradCAM:
Chapter 19, Learner:
Chapter 20, conclusion:

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