BI NMA 04: Deep Learning Basics Panel

BI NMA 04:

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

• Amita Kapoor


• Lyle Ungar
  
  
  
  • @LyleUngar
  
  
  
  


• Surya Ganguli
  
  
  
  • @SuryaGanguli
  
  
  
  

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: