Each lab is designed around practical cases and covers an independent topic.
You can choose to follow one or all of them depending on your interest.
The labs are supervised by at least 3 subject matter experts.
To attend the Expert labs, you are expected to have a basic to intermediate level on the topic.
If you are a beginner, the only chance to possibly be able to follow the labs is first to watch, before the school, all the webinars we have recorded (accessible upon registration), then to attend to all the labs, and to already have strong basis in Python.
For the registration rates to the labs, please see the Registration page.
Lab 1 | July 12th
The first lab will be dedicated to audio data analysis and speech recognition. In this lab, we will experiment how deep learning works with audio signals; more specifically, we will learn how to build and train some efficient deep learning models to recognize speech by combining CNNs, RNNs, and Attention mechanisms.
Lab 2 | July 13th
The second lab will be dedicated to Deep Reinforcement learning. This lab is meant to provide a first experience on using Deep Reinforcement Learning (DRL), for both synthetic and more realistic problems. While applications of RL are typically limited to discrete, low-dimensional constraints, recent advances in Deep RL (DQN for Atari 2600, AlphaGo, and more lately AlphaGo Zero) have demonstrated human-level or super-human performance in complex, high-dimensional spaces.
Lab 3 | July 14th
In the third lab we will see some well-known deep architectures for image classification and object detection. We will use these models to learn domain adaptation in real-life scenarios.
Lab 4 | July 15th
The fourth lab, we will implement several likelihood-based deep generative models. We will focus on variational autoencoders (VAEs) and variations thereof, and will also discuss normalizing flows and autoregressive models. Applications will include dimensionality reduction and missing data imputation.
Lab 5 | July 16th
The fifth lab will start with a brief introduction to the available GNN frameworks, then how to represent a graph, and how to define a GNN Model in the available frameworks. We will then explore learning tasks such as Node Classification, Graph Classification, or Link Prediction. We will finish with one or two projects among Graph Visualization, Subgraph matching, Clique detection and communities, Learning PageRank.