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


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 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: https://colab.research.google.com/github/fastai/fastbook/blob/master/app_jupyter.ipynb
Chapter 1, Intro: https://colab.research.google.com/github/fastai/fastbook/blob/master/01_intro.ipynb
Chapter 2, Production: https://colab.research.google.com/github/fastai/fastbook/blob/master/02_production.ipynb
Chapter 3, Ethics: https://colab.research.google.com/github/fastai/fastbook/blob/master/03_ethics.ipynb
Chapter 4, MNIST Basics: https://colab.research.google.com/github/fastai/fastbook/blob/master/04_mnist_basics.ipynb
Chapter 5, Pet Breeds: https://colab.research.google.com/github/fastai/fastbook/blob/master/05_pet_breeds.ipynb
Chapter 6, Multi-Category: https://colab.research.google.com/github/fastai/fastbook/blob/master/06_multicat.ipynb
Chapter 7, Sizing and TTA: https://colab.research.google.com/github/fastai/fastbook/blob/master/07_sizing_and_tta.ipynb
Chapter 8, Collab: https://colab.research.google.com/github/fastai/fastbook/blob/master/08_collab.ipynb
Chapter 9, Tabular: https://colab.research.google.com/github/fastai/fastbook/blob/master/09_tabular.ipynb
Chapter 10, NLP: https://colab.research.google.com/github/fastai/fastbook/blob/master/10_nlp.ipynb
Chapter 11, Mid-Level API: https://colab.research.google.com/github/fastai/fastbook/blob/master/11_midlevel_data.ipynb
Chapter 12, NLP Deep-Dive: https://colab.research.google.com/github/fastai/fastbook/blob/master/12_nlp_dive.ipynb
Chapter 13, Convolutions: https://colab.research.google.com/github/fastai/fastbook/blob/master/13_convolutions.ipynb
Chapter 14, Resnet: https://colab.research.google.com/github/fastai/fastbook/blob/master/14_resnet.ipynb
Chapter 15, Arch Details: https://colab.research.google.com/github/fastai/fastbook/blob/master/15_arch_details.ipynb
Chapter 16, Optimizers and Callbacks: https://colab.research.google.com/github/fastai/fastbook/blob/master/16_accel_sgd.ipynb
Chapter 17, Foundations: https://colab.research.google.com/github/fastai/fastbook/blob/master/17_foundations.ipynb
Chapter 18, GradCAM: https://colab.research.google.com/github/fastai/fastbook/blob/master/18_CAM.ipynb
Chapter 19, Learner: https://colab.research.google.com/github/fastai/fastbook/blob/master/19_learner.ipynb
Chapter 20, conclusion: https://colab.research.google.com/github/fastai/fastbook/blob/master/20_conclusion.ipynb

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