Pytorch tutorial: Autoencoders

Deep Learning on Graphs(3/3): Graph embedding

Deep Learning on Graphs(2/3): Signal processing on graphs

Deep Learning on Graphs(1/3): Node embedding

Basics of Deep Learning: Transfer Learning

Basics of Deep Learning: Resnets

Basics of Deep Learning: Batchnorm

Basics of Deep Learning: Dropout

Basics of Deep Learning: Problems with Depth

Basics of Deep Learning: Benefits of Depth

Basics of Deep Learning: Siamese Representation Learning

Pytorch tutorial on batch for sequences

Introduction to Julia: Automatic differentiation with dual numbers

Privacy Preserving Machine Learning by Daniel Huynh

Pytorch tutorial: Recurrent Neural Networks practice

Pytorch tutorial: Recurrent Neural Networks theory

Pytorch tutorial: Generative adversarial networks (GAN)

Pytorch tutorial: Autoencoders

Pytorch tutorial: Collaborative filtering

Pytorch tutorial: Embedding layers and dataloaders

Pytorch tutorial: Convolutional neural network

Pytorch tutorial: writing a PyTorch module

Pytorch tutorial: Optimization for deep leaning

Pytorch tutorial: Loss functions

Pytorch tutorial: PyTorch tensors