Deep Learning
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 |
2017 | The most beginner-friendly intro deep learning course. | Easy | ||
2019 | The most practical intro deep learning course. | Easy-Medium | ||
2020 | The best all-around intro DL course for people who have had prior exposure to ML. | Medium |
Resource | Year | Description | Type |
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. | 📚 | |
2020 | A book that is complementary to the fast.ai courses, covering deep learning, fast.ai and lots of examples. | 📚 | |
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. | 📚 | |
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. | 🎥 | |
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. | 🎥 | |
2017 | The most visually intuitive introduction to neural networks, gradient descent and backpropagation. | 🎥 | |
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). | 🎥 |
Last modified 1yr ago