Deep Learning Basics Panel

This is the 4th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the first of 3 in the deep learning series. In this episode, the panelists discuss their experiences with some basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.

Guests

The other panels:

First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.
Second panel, about linear systems, real neurons, and dynamic networks.
Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.
Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

Timestamps: