Basics of Deep Learning
This course presents practical details of deep learning architectures, in which we’ll attempt to demystify deep learning and kick-start you into using it in your own field of interest.
During this course, you will gain a better understanding of the basis of deep learning and get familiar with its applications. Indeed, we will show you how to set up, train, debug and visualize your own neural networks.
After following this course, you will be able to understand papers, blog posts and code available online, and adapt them to your own projects. This is why we do not use high-level neural networks APIs and focus on the PyTorch library.
Mathematics: basics of linear algebra, probability, differential calculus and optimization Programming: basic proficiency Python
To have access to the solutions of the practicals, you need to sign in to the forum.
- Lesson 1 (April 7): Machine learning pipeline and course overview:
- Lesson 2 (April 14): PyTorch tensors and automatic differentiation
- Lesson 3 (April 21): Classification with deep learning
- Lesson 4 (April 24): Convolutional neural networks
- Lesson 5 (April 28): Embedding layers and dataloaders
- video (part1) Embedding layers and dataloaders
- slides Embedding layers and dataloaders
- video (part2) Collaborative filtering
- notebook and colab Collaborative filtering solution (forum login required)
- practicals and colab refactoring the code seen above for a larger dataset. solution (forum login required)
- Lesson 6 (May 5): Unsupervised learning: auto-encoders and generative adversarial networks
- video (part 1) Autoencoders
- slides Autoencoders
- practicals and colab Denoising Autoencoder solution (forum login required)
- video (part 2) Generative Adversarial Network (GAN)
- slides Generative Adversarial Network (GAN)
- practicals and colab conditional GAN and (simplified) InfoGAN solution (forum login required)
- Lesson 7 (May 12): Recurrent neural networks