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.
๐ 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
โญ๏ธ Course Contents โญ๏ธ
(See next section for book & code.)
โจ๏ธ (0:00:00) Lesson 1 – Your first modules
โจ๏ธ (1:22:55) Lesson 2 – Evidence and p values
โจ๏ธ (2:53:59) Lesson 3 – Production and Deployment
โจ๏ธ (5:00:20) Lesson 4 – Stochastic Gradient Descent (SGD) from scratch
โจ๏ธ (7:01:56) Lesson 5 – Data ethics
โจ๏ธ (9:09:46) Lesson 6 – Collaborative filtering
โจ๏ธ (https://youtu.be/HL7LOfyf6bc) Lesson 7 – Tabular data
โจ๏ธ (https://youtu.be/HL7LOfyf6bc) Lesson 8 – Natural language processing
โญ๏ธ Book chapters and code on Google Colab โญ๏ธ
๐ 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).
๐ป Intro to Jupyter: https://colab.research.google.com/github/fastai/fastbook/blob/master/app_jupyter.ipynb
๐ป Chapter 1, Intro: https://colab.research.google.com/github/fastai/fastbook/blob/master/01_intro.ipynb
๐ป Chapter 2, Production: https://colab.research.google.com/github/fastai/fastbook/blob/master/02_production.ipynb
๐ป Chapter 3, Ethics: https://colab.research.google.com/github/fastai/fastbook/blob/master/03_ethics.ipynb
๐ป Chapter 4, MNIST Basics: https://colab.research.google.com/github/fastai/fastbook/blob/master/04_mnist_basics.ipynb
๐ป Chapter 5, Pet Breeds: https://colab.research.google.com/github/fastai/fastbook/blob/master/05_pet_breeds.ipynb
๐ป Chapter 6, Multi-Category: https://colab.research.google.com/github/fastai/fastbook/blob/master/06_multicat.ipynb
๐ป Chapter 7, Sizing and TTA: https://colab.research.google.com/github/fastai/fastbook/blob/master/07_sizing_and_tta.ipynb
๐ป Chapter 8, Collab: https://colab.research.google.com/github/fastai/fastbook/blob/master/08_collab.ipynb
๐ป Chapter 9, Tabular: https://colab.research.google.com/github/fastai/fastbook/blob/master/09_tabular.ipynb
๐ป Chapter 10, NLP: https://colab.research.google.com/github/fastai/fastbook/blob/master/10_nlp.ipynb
๐ป Chapter 11, Mid-Level API: https://colab.research.google.com/github/fastai/fastbook/blob/master/11_midlevel_data.ipynb
๐ป Chapter 12, NLP Deep-Dive: https://colab.research.google.com/github/fastai/fastbook/blob/master/12_nlp_dive.ipynb
๐ป Chapter 13, Convolutions: https://colab.research.google.com/github/fastai/fastbook/blob/master/13_convolutions.ipynb
๐ป Chapter 14, Resnet: https://colab.research.google.com/github/fastai/fastbook/blob/master/14_resnet.ipynb
๐ป Chapter 15, Arch Details: https://colab.research.google.com/github/fastai/fastbook/blob/master/15_arch_details.ipynb
๐ป Chapter 16, Optimizers and Callbacks: https://colab.research.google.com/github/fastai/fastbook/blob/master/16_accel_sgd.ipynb
๐ป Chapter 17, Foundations: https://colab.research.google.com/github/fastai/fastbook/blob/master/17_foundations.ipynb
๐ป Chapter 18, GradCAM: https://colab.research.google.com/github/fastai/fastbook/blob/master/18_CAM.ipynb
๐ป Chapter 19, Learner: https://colab.research.google.com/github/fastai/fastbook/blob/master/19_learner.ipynb
๐ป Chapter 20, conclusion: https://colab.research.google.com/github/fastai/fastbook/blob/master/20_conclusion.ipynb
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