Welcome to the Weizmann practical Deep Learning Course 2024
All communication with the lecturers will be made via SLACK. Join here:
Your lecturers and TAs are
Etienne Dreyer, Nilotpal Kakati, Dmitrii Kobylianskii and Prof Eilam Gross.
The grading system is based on your mandatory homework assignments (10%), a project (<30%), and a takehome assignment (>60%). The exact weight of the project and takehome assignment will be fixed so the class average will not exceed 90.
All lecture slides and tutorial code will be posted below.
Date  Lecture  Slides & Video 
Tutorial  Slides & Video 
Tutorial 2  Slides & Video 

18/4/2024 
Eilam: Introduction 
Etienne: Python essentials 

Etienne: NumPy and grad. desc. 

*2/5/2024 
Dmitrii: Backpropagation 
Nilotpal: pytorch (tut) 
Nilotpal: homework 1 Classification 

9/5/2024 
Eilam: Convolutional NN 
Nilotpal: CNNs (tut) 
notebooks 
Dmitrii: Optimisation, 

*16/5/2024 
Etienne: Autoencoders 
Nilotpal: VAE (tut) 
Etienne: transfer learning homework 2 

23/5/2024 
Eilam: CNN architectures and RNN 
Eilam: GAN 
Nilotpal: DCGAN tutorial 


**30/5/2024 
Nilotpal: Attention is All You Need 

Eilam: Attenion on Attention 
Nilotpal: Attention tutorial 

**6/6/2024 
Eilam: Detection & Segmentation 

Nilotpal: UNET

Dmitrii/Nilotpal: Homework 3: 


*^13/6/2024 
Eilam (zoom?): Graph Neural Networks 

Etienne: GNN tutorial 

Etienne: homework 4: Graph Neural Networks 

20/6/2024 
Nilotpal: Diffusion 

Dmitrii: Diffusion tutorial 
Nilotpal: Bayesian NN 


27/6/2024 
Eilam: Deep Reinforcement Learning 

Dmitrii: Deep Qlearning tutorial


Etienne: Homework 5: policy gradient 

4/7/2024  Project Proposals I  
11/7/2024  Project Proposals II.  
TBA 
Take Home Assignement  
TBA 11:0016:00 
Project Presentations (POSTERs festival) 