Brain Inspired

BI NMA 04: Deep Learning Basics Panel


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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:

           

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          Brain InspiredBy Paul Middlebrooks

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