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Neuromatch Academy: Deep Learning
Introduction
Schedule
General schedule
Shared calendars
Timezone widget
Technical Help
Using jupyterbook
Using Google Colab
Using Kaggle
Using Discord
The Basics
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
Multi Layer Perceptrons (W1D3)
Tutorial 1: Biological vs. Artificial Neural Networks
Tutorial 2: Deep MLPs
Optimization (W1D4)
Tutorial 1: Optimization techniques
Regularization (W1D5)
Tutorial 1: Regularization techniques part 1
Tutorial 2: Regularization techniques part 2
Deep Learning: The Basics Wrap-up
Doing More With Fewer Parameters
Convnets And Recurrent Neural Networks (W2D1)
Tutorial 1: Introduction to CNNs
Tutorial 2: Introduction to RNNs
Modern Convnets (W2D2)
Tutorial 1: Learn how to use modern convnets
(Bonus) Tutorial 2: Facial recognition using modern convnets
Modern Recurrent Neural Networks (W2D3)
Tutorial 1: Modeling sequencies and encoding text
Tutorial 2: Modern RNNs and their variants
Attention And Transformers (W2D4)
Tutorial 1: Learn how to work with Transformers
Generative Models (W2D5)
Tutorial 1: Variational Autoencoders (VAEs)
Tutorial 2: Introduction to GANs
Tutorial 3: Conditional GANs and Implications of GAN Technology
(Bonus) Tutorial 4: Deploying Neural Networks on the Web
Deep Learning: Doing more with fewer parameters Wrap-up
Advanced Topics
Unsupervised And Self Supervised Learning (W3D1)
Tutorial 1: Un/Self-supervised learning methods
Basic Reinforcement Learning (W3D2)
Tutorial 1: Introduction to Reinforcement Learning
Reinforcement Learning For Games (W3D3)
Tutorial 1: Learn to play games with RL
Continual Learning (W3D4)
Tutorial 1: Introduction to Continual Learning
Tutorial 2: Out-of-distribution (OOD) Learning
Deep Learning: Advanced Topics Wrap-up
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
Image Alignment
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
repository
open issue
.md
.pdf
Natural Language Processing
Natural Language Processing
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