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Prerequisites and preparatory materials for NMA Deep Learning
Basics And Pytorch (W1D1)

Linear Deep Learning (W1D2)

Multi Layer Perceptrons (W1D3)

Optimization (W1D5)

Regularization (W2D1)

Deep Learning: The Basics and Fine Tuning Wrap-up

Convnets And Dl Thinking (W2D2)

Modern Convnets (W2D3)

Generative Models (W2D4)

Attention And Transformers (W2D5)

Time Series And Natural Language Processing (W3D1)

Dl Thinking2 (W3D2)

Deep Learning: Convnets and NLP

Unsupervised And Self Supervised Learning (W3D3)

Basic Reinforcement Learning (W3D4)

Reinforcement Learning For Games And Dl Thinking3 (W3D5)

Deploy Models (Bonus)

Daily guide for projects

Modeling Step-by-Step Guide

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Site Navigation

Schedule
Technical Help
Quick links and policies
Prerequisites and preparatory materials for NMA Deep Learning
Basics And Pytorch (W1D1)

Linear Deep Learning (W1D2)

Multi Layer Perceptrons (W1D3)

Optimization (W1D5)

Regularization (W2D1)

Deep Learning: The Basics and Fine Tuning Wrap-up

Convnets And Dl Thinking (W2D2)

Modern Convnets (W2D3)

Generative Models (W2D4)

Attention And Transformers (W2D5)

Time Series And Natural Language Processing (W3D1)

Dl Thinking2 (W3D2)

Deep Learning: Convnets and NLP

Unsupervised And Self Supervised Learning (W3D3)

Basic Reinforcement Learning (W3D4)

Reinforcement Learning For Games And Dl Thinking3 (W3D5)

Deploy Models (Bonus)

Daily guide for projects

Modeling Step-by-Step Guide

Project Templates

Models and Data sets

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- Using jupyterbook
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Prerequisites and preparatory materials for NMA Deep Learning

Basics Module

Basics And Pytorch (W1D1)
- Tutorial 1: PyTorch
Linear Deep Learning (W1D2)
Multi Layer Perceptrons (W1D3)
- Tutorial 1: Biological vs. Artificial Neural Networks
- Tutorial 2: Deep MLPs

Fine Tuning

Optimization (W1D5)
- Tutorial 1: Optimization techniques
Regularization (W2D1)
- Tutorial 1: Regularization techniques part 1
- Tutorial 2: Regularization techniques part 2
Deep Learning: The Basics and Fine Tuning Wrap-up

ConvNets and Generative Models

Convnets And Dl Thinking (W2D2)
Modern Convnets (W2D3)
- Tutorial 1: Learn how to use modern convnets
- Bonus Tutorial: Facial recognition using modern convnets
Generative Models (W2D4)

Natural Language Processing

Attention And Transformers (W2D5)
- Tutorial 1: Learn how to work with Transformers
- Bonus Tutorial: Understanding Pre-training, Fine-tuning and Robustness of Transformers
Time Series And Natural Language Processing (W3D1)
Dl Thinking2 (W3D2)
- Tutorial 1: Deep Learning Thinking 2: Architectures and Multimodal DL thinking
Deep Learning: Convnets and NLP

Unsupervised and Reinforcement Learning

Unsupervised And Self Supervised Learning (W3D3)
- Tutorial 1: Un/Self-supervised learning methods
- Bonus Lecture: Melanie Mitchell
Basic Reinforcement Learning (W3D4)
- Tutorial 1: Basic Reinforcement Learning
- Bonus Lecture: Chealsea Finn
Reinforcement Learning For Games And Dl Thinking3 (W3D5)

Deploy Models on the Web

Deploy Models (Bonus)
- Bonus Tutorial: Deploying Neural Networks on the Web

Project Booklet

Introduction to projects
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Modeling Step-by-Step Guide
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Deep Learning: Convnets and NLP

Contents

Podcast Panel Discussion

Deep Learning: Convnets and NLP#

Podcast Panel Discussion#

Neuromatch Academy has collaborated with Paul Middlebrooks of the podcast Brain Inspired to host a series of panel discussions on the content modules.

The fifth session covers the Deep Learning Convolutional Neural Networks and Natural Language Processing modules, featuring panelists Brad Wyble, Kyunghyun Cho, He He, and João Sedoc.

Listen here or in the embedded audio below!

# @markdown
from IPython.display import IFrame
IFrame(src='https://braininspired.castos.com/player/612309', width='100%', height='150')

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Unsupervised And Self Supervised Learning

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Podcast Panel Discussion

By Neuromatch

Last updated on None.

The contents of this repository are shared under under a Creative Commons Attribution 4.0 International License. Software elements are additionally licensed under the BSD (3-Clause) License.