Deep Learning course taught at YSDA and HSE @fall'21.

Lecture, practice materials and completed assignments for each week are in ./week* folders.

Syllabus

• week01 Intro to deep learning

  • Lecture: Deep learning -- introduction, backpropagation algorithm, adaptive optimization methods
  • Seminar: Neural networks in numpy

• week02 Catch-all lecture about deep learning tricks

  • Lecture: Deep learning as a language, dropout, batch/layer normalization, other tricks, deep learning frameworks
  • Seminar: PyTorch basics

• week03 Convolutional neural networks

  • Lecture: Computer vision tasks, Convolution and Pooling layers, ConvNet architectures, Data Augmentation
  • Seminar: Training your first ConvNet

• week04 Transfer Learning

• week05 NLP, RNNs

• week06 RNNs

• week07 Seq2seq and Attention

• week08 GANs