Deep Learning with Catalyst

This is an open deep learning course made by Deep Learning School, Tinkoff and Catalyst team. Lectures and practice notebooks located in '''./week*''' folders. Homeworks are in '''./homework*''' folders.

Syllabus

• week 1: Deep learning intro
  • Deep learning – introduction, backpropagation algorithm. Optimization methods.
  • Neural Network in numpy.
• week 2: Deep learning frameworks
  • Regularization methods and deep learning frameworks.
  • Pytorch basics & extras.
• week 3: Convolutional Neural Network
  • CNN. Model Zoo.
  • Convolutional kernels. ResNet. Simple Noise Attack.
• week 4: Object Detection, Image Segmentation
  • Object Detection. (One, Two)-Stage methods. Anchors.
  • Image Segmentation. Up-scaling. FCN, U-net, FPN. DeepMask.
• week 5: Metric Learning
  • Metric Learning. Contrastive and Triplet Loss. Samplers.
  • Cross Entropy Loss modifications. SphereFace, CosFace, ArcFace.
• week 6: Autoencoders
  • AutoEncoders. Denoise, Sparse, Variational.
  • Generative Models. Autoregressive models.
• week 7: Generative Adversarial Models
  • Generative Adversarial Networks. VAE-GAN. AAE.
  • Energy based model.
• week 8: Natural Language Processing
  • Embeddings.
  • RNN. LSTM, GRU.
• week 9: Attention and transformer model
  • Attention Mechanism.
  • Transformer Model.
• week 10: Transfer Learning in NLP
  • Pretrained Transformers. BERT. GPT.
  • Data Augmentation in Texts. Domain Adaptation.
• week 11: Recommender Systems
  • Collaborative Filtering. FunkSVD.
  • Neural Collaborative Filtering.
• week 12: Reinforcement Learning for RecSys
  • Reinforcement Learning. DQN Algorithm.
  • DDPG Algorithm. Wolpertinger.
• week 13: Extras
  • Research & Deploy.
  • Config API. Reaction.

Course staff & contributors

• @artek0chumak
• @Scitator
• @elephantmipt
• @lordofprograms
• @AlexeySh
• @Inkln
• @zelcookie