The content should primarily be accessed from our new ebook: https://deeplearning.neuromatch.io/ [under continuous development]

August 2-20, 2021

Objectives: Gain hands-on, code-first experience with deep learning theories, models, and skills that are useful for applications and for advancing science. We focus on how to decide which problems can be tackled with deep learning, how to determine what model is best, how to best implement a model, how to visualize / justify findings, and how neuroscience can inspire deep learning. And throughout we emphasize the ethical use of DL.

Please check out expected prerequisites here!

Confirmed speakers:

• Amita Kapoor (U Delhi)
• Anima Anandkumar (Caltech)
• Aude Oliva (MIT)
• Chelsea Finn (Stanford)
• Emily Denton (Google)
• Geoffrey Hinton (U Toronto)
• Joao Sedoc (NYU)
• Kyunghyun Cho (NYU)
• Melanie Mitchell (Santa Fe Institute)
• Yann LeCun (Facebook)
• Yoshua Bengio (MILA)

Coming soon... stay tuned...

coordinated by Konrad Kording (U Penn)

Description Welcome, introduction to Google Colab, meet and greet, a bit of DL history, DL basics and introduction to Pytorch

coordinated by Andrew Saxe (Oxford)

Description Gradients, AutoGrad, linear regression, concept of optimization, loss functions, designing deep linear systems and how to train them

coordinated by Surya Ganguli (Stanford)

Description From neuroscience inspiration, to solving the XOR problem, to function approximation, cross-validation, training, and trade-offs

coordinated by Ioannis Mitliagkas (MILA)

Description Why optimization is hard and all the tricks to get it to work

coordinated by Lyle Ungar (U Penn)

Description The problem of overfitting and different ways to solve it

coordinated by Alona Fyshe (U Alberta)

Description How the number of parameters affects generalization, and what Convolutional Neural Networks (Convnets) and Recurrent Neural Networks (RNNs) can do for you to help

coordinated by Alexander Ecker (U Goettingen)

Description Modern Convolutional Neural Nets and how to use them for Transfer Learning

coordinated by James Evans (DeepAI)

Description Memory, time series, recurrence, vanishing gradients and embeddings

coordinated by He He (NYU)

Description How attention helps classification, encoding and decoding

coordinated by Vikash Gilja (UCSD) and Akash Srivastava (MIT-IBM)

Description Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs) as methods for representing latent data statistics

coordinated by Project TAs

coordinated by Blake Richards (McGill) and Tim Lillicrap (Google DeepMind)

Description Learning without direct supervision

coordinated by Jane Wang (Google DeepMind) and Feryal Behbahani (Google DeepMind)

Description How RL can help solve DL problems

coordinated by Tim Lillicrap (Google DeepMind) and Blake Richards (McGill)

Description Get to learn how RL solved the game of Go

coordinated by Joshua T. Vogelstein (Johns Hopkins) and Vincenzo Lomonaco (U Pisa)

Description How can we get a causality, how to generalize out of sample, what will the future bring?

Description After the tutorials the day is dedicated to group projects and celebrating course completion