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Prerequisites and preparatory materials for NMA Deep Learning
Basics And Pytorch (W1D1)
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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)
Introduction
Daily guide for projects
Modeling Step-by-Step Guide
Project Templates
Models and Data sets
Site Navigation
Schedule
Technical Help
Quick links and policies
Prerequisites and preparatory materials for NMA Deep Learning
Basics And Pytorch (W1D1)
More
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)
Introduction
Daily guide for projects
Modeling Step-by-Step Guide
Project Templates
Models and Data sets
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+
K
Introduction
Schedule
General schedule
Shared calendars
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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)
