I just sat down this morning and organized all deep learning related videos I recorded in 2021. I am sure this will be a useful reference for my future self, but I am also hoping it might be useful for one or the other person out there.

PS: All code examples are in PyTorch :)

Table of Contents

• Part 1: Introduction
  • L01: Introduction to deep learning
  • L02: The brief history of deep learning
  • L03: Single-layer neural networks: The perceptron algorithm
• Part 2: Mathematical and computational foundations
  • L04: Linear algebra and calculus for deep learning
  • L05: Parameter optimization with gradient descent
  • L06: Automatic differentiation with PyTorch
  • L07: Cluster and cloud computing resources
• Part 3: Introduction to neural networks
  • L08: Multinomial logistic regression / Softmax regression
  • L09: Multilayer perceptrons and backpropration
  • L10: Regularization to avoid overfitting
  • L11: Input normalization and weight initialization
  • L12: Learning rates and advanced optimization algorithms
• Part 4: Deep learning for computer vision and language modeling
  • L13: Introduction to convolutional neural networks
  • L14: Convolutional neural networks architectures
  • L15: Introduction to recurrent neural networks
• Part 5: Deep generative models
  • L16: Autoencoders
  • L17: Variational autoencoders
  • L18: Introduction to generative adversarial networks
  • L19: Self-attention and transformer networks

Part 1: Introduction

L01: Introduction to deep learning

L02: The brief history of deep learning

L03: Single-layer neural networks: The perceptron algorithm

Part 2: Mathematical and computational foundations

L04: Linear algebra and calculus for deep learning

L05: Parameter optimization with gradient descent

L06: Automatic differentiation with PyTorch

L07: Cluster and cloud computing resources

Part 3: Introduction to neural networks

L08: Multinomial logistic regression / Softmax regression

L09: Multilayer perceptrons and backpropration

L10: Regularization to avoid overfitting

L11: Input normalization and weight initialization

L12: Learning rates and advanced optimization algorithms

Part 4: Deep learning for computer vision and language modeling

L13: Introduction to convolutional neural networks

L14: Convolutional neural networks architectures

L15: Introduction to recurrent neural networks

Part 5: Deep generative models

L16: Autoencoders

L17: Variational autoencoders

L18: Introduction to generative adversarial networks

L19: Self-attention and transformer networks