Deep Learning

DL Courses

Prerequisites: Foundation courses.
Recommended path
    The most approachable introduction to DL is a combination of deeplearning.ai and fast.ai. Use fast.ai as a practical bottom-up course and deeplearning.ai to build on the theoretical aspects taught in the fast.ai course.
    For people who are looking for a more mathematical introduction, NYU's Deep Learning course is the perfect choice. It teaches theory and offers coding practice in a really intuitive but also rigorous way.
Course
Year
Description
Difficulty Level
Resources
deep learning.ai - Deep Learning Specialization​
2017
The most beginner-friendly intro deep learning course.
Easy
2019
The most practical intro deep learning course.
Easy-Medium
β€‹πŸ€–β€‹
2020
The best all-around intro DL course for people who have had prior exposure to ML.
Medium
​

Supplementary Resources

Resource
Year
Description
Type
Goodfellow, Bengio, Courville - Deep Learning​
2016
The most comprehensive book on the foundational topics in deep learning. For companion videos of most of the chapters, check out this YouTube playlist.
πŸ“š
fast.ai - book draft​
2020
A book that is complementary to the fast.ai courses, covering deep learning, fast.ai and lots of examples.
πŸ“š
2019, 2020
The content of the book is similar to the Deep Learning book but a lot more practically oriented with lots of exercises in PyTorch/TensorFlow.
πŸ“š
Stanford - CS230 Deep Learning​
2018
A set of introductory lectures on Deep Learning by Andrew Ng with a focus on practical issues such as reading research papers, career advice and ML in healthcare.
πŸŽ₯
2019, 2020, 2021
Also a great course, but not as comprehensive as the ones in the course list. These lectures can be a great first exposure to the topics and also have great guest lectures.
πŸŽ₯
3Blue1Brown - Neural Networks​
2017
The most visually intuitive introduction to neural networks, gradient descent and backpropagation.
πŸŽ₯
Hugo Larochelle - Neural Networks​
2013
An older course on deep learning from 2013, which covers topics that are not usually covered in more recent courses (e.g. conditional random fields, restricted Boltzman machines and sparse coding).
πŸŽ₯
Last modified 5mo ago