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catalyst-team
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Description

Deep Learning with Catalyst

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Deep Learning with Catalyst Stepik Slack

dls-catalyst-course

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

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