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Neuromatch Academy: Deep Learning (Experimental)
  • Introduction
  • Schedule
    • General schedule
    • Shared calendars
    • Timezone widget
  • Technical Help
    • Using jupyterbook
      • Using Google Colab
      • Using Kaggle
    • Using Discord
  • 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)
    • Tutorial 1: Gradient Descent and AutoGrad
    • Tutorial 2: Learning Hyperparameters
    • Tutorial 3: Deep linear neural networks
    • Bonus Lecture: Yoshua Bengio
  • 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)
    • Tutorial 1: Introduction to CNNs
    • Tutorial 2: Deep Learning Thinking 1: Cost Functions
    • Bonus Lecture: Kyunghyun Cho
  • Modern Convnets (W2D3)
    • Tutorial 1: Learn how to use modern convnets
    • Bonus Tutorial: Facial recognition using modern convnets
  • Generative Models (W2D4)
    • Tutorial 1: Variational Autoencoders (VAEs)
    • Tutorial 2: Diffusion models
    • Tutorial 3: Image, Conditional Diffusion and Beyond
    • Bonus Lecture: Geoffrey Hinton

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)
    • Tutorial 1: Introduction to processing time series
    • Tutorial 2: Natural Language Processing and LLMs
    • Bonus Tutorial: Multilingual Embeddings
  • 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)
    • Tutorial 1: Reinforcement Learning For Games
    • Tutorial 2: Deep Learning Thinking 3
    • Bonus Tutorial: Planning with Monte Carlo Tree Search
    • Bonus Lecture: Amita Kapoor

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
    • Modeling Steps 1 - 2
    • Modeling Steps 3 - 4
    • Modeling Steps 5 - 6
    • Modeling Steps 7 - 9
    • Modeling Steps 10
    • Example Data Project: the Train Illusion
    • Example Model Project: the Train Illusion
    • Example Deep Learning Project
  • Project Templates
    • Computer Vision
      • Slides
      • Ideas
      • Knowledge Extraction from a Convolutional Neural Network
      • Music classification and generation with spectrograms
      • Something Screwy - image recognition, detection, and classification of screws
      • Data Augmentation in image classification models
      • Transfer Learning
    • Reinforcement Learning
      • Slides
      • Ideas
      • NMA Robolympics: Controlling robots using reinforcement learning
      • Performance Analysis of DQN Algorithm on the Lunar Lander task
      • Using RL to Model Cognitive Tasks
    • Natural Language Processing
      • Slides
      • Ideas
      • Twitter Sentiment Analysis
      • Machine Translation
    • Neuroscience
      • Slides
      • Ideas
      • Animal Pose Estimation
      • Segmentation and Denoising
      • Load algonauts videos
      • Vision with Lost Glasses: Modelling how the brain deals with noisy input
      • Moving beyond Labels: Finetuning CNNs on BOLD response
      • Focus on what matters: inferring low-dimensional dynamics from neural recordings
  • Models and Data sets
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Using Discord

Using DiscordΒΆ

Please click here for Discord Guide

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Using Kaggle

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Quick links and policies

By Neuromatch

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.