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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 (W1D4)

Regularization (W2D1)

Deep Learning: The Basics and Fine Tuning Wrap-up

Convnets (W2D2)

Generative Models And Deep Learning Discussion 1 (W2D3)

Diffusion Generative Models (W2D4)

Time Series And Natural Language Processing (W2D5)

Attention And Transformers (W3D1)

Deep Learning Discussion 2 (W3D2)

Deep Learning: Convnets and NLP

Unsupervised And Self Supervised Learning (W3D3)

Basic Reinforcement Learning (W3D4)

Advanced Reinforcement Learning And Deep Learning Discussion 3 (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 (W1D4)

Regularization (W2D1)

Deep Learning: The Basics and Fine Tuning Wrap-up

Convnets (W2D2)

Generative Models And Deep Learning Discussion 1 (W2D3)

Diffusion Generative Models (W2D4)

Time Series And Natural Language Processing (W2D5)

Attention And Transformers (W3D1)

Deep Learning Discussion 2 (W3D2)

Deep Learning: Convnets and NLP

Unsupervised And Self Supervised Learning (W3D3)

Basic Reinforcement Learning (W3D4)

Advanced Reinforcement Learning And Deep Learning Discussion 3 (W3D5)

Deploy Models (Bonus)

Daily guide for projects

Modeling Step-by-Step Guide

Project Templates

Models and Data sets

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Introduction

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Technical Help
- Using jupyterbook
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Quick links and policies

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 (W1D4)
- 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 (W2D2)
Generative Models And Deep Learning Discussion 1 (W2D3)
Diffusion Generative Models (W2D4)
- Tutorial 1: Diffusion models
- Tutorial 2: Image Diffusion

Natural Language Processing

Time Series And Natural Language Processing (W2D5)
Attention And Transformers (W3D1)
- Tutorial 1: Learn how to work with Transformers
- Bonus Tutorial: Understanding Pre-training, Fine-tuning and Robustness of Transformers
Deep Learning Discussion 2 (W3D2)
- Tutorial 1: Deep Learning Discussion 2: Architectures and Multimodal DL
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)
Advanced Reinforcement Learning And Deep Learning Discussion 3 (W3D5)
- Tutorial 1: Deep Learning Discussion 3
- Bonus Tutorial: Reinforcement Learning For Games

Deploy Models on the Web

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

Project Booklet

Introduction to projects
Daily guide for projects
Modeling Step-by-Step Guide
Project Templates
Models and Data sets

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Diffusion Generative Models

Diffusion Generative Models#

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By Neuromatch

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