The Relationship Between Optimization and Regularization

BI NMA 04:

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

• 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: