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

Artis Modus



freeCodeCamp.org

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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32 thoughts on “Practical Deep Learning for Coders – Full Course from fast.ai and Jeremy Howard”

  2. Question: At 14:14 , how is it possible that the 2nd layer of neurons could learn any complex function (non-linear) like XOR? Normally adding up linear layers will result in a linear model. Can someone explain this, please?

  4. For the code to work in colab, do "!pip install fastai –upgrade" first and then change "from fastai2.vision.all import *" to "from fastai.vision.all import *"

  7. results = search_images_bing(key, 'grizzly bear')
    ims = results.attrgot('contentUrl')
    len(ims)
    Can everybody run this piece of code okay? I have HTTPError: 401 Client Error:

  11. Serious questions, why all smart people have spiky hair. I listen to them more attentively. Jokes apart, really appreciate the generosity of Jeremy, Rachael, Sylvain and the entire team also freecodecamp.

  17. Random passive aggressive anti WYT statements by the female sjvv detracted a lot from this. Hopefully some of us use fast AI to help disprove the so called gender wage gap and other w0ke garbage theory

  19. Overfitting, like a racer only on one track,

    After training to a certain extent, the adaptability to other runways and road will decrease

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