Catalyst

Catalyst is a PyTorch framework for Deep Learning Research and Development.

It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write yet another train loop.

Break the cycle - use the Catalyst!

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Course

Week 1: Deep learning intro

Week 1: Deep learning intro

Deep learning – introduction, backpropagation algorithm. Optimization methods. Neural Network in numpy.

Week 2: Deep learning frameworks

Week 2: Deep learning frameworks

Regularization methods and deep learning frameworks. Pytorch basics & extras.

Week 3: Convolutional Neural Networks

Week 3: Convolutional Neural Networks

CNN. Model Zoo. Convolutional kernels. ResNet. Simple Noise Attack.

Week 4: Object Detection, Image Segmentation

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

Week 5: Metric Learning

Metric Learning. Contrastive and Triplet Loss. Samplers. Cross Entropy Loss modifications. SphereFace, CosFace, ArcFace.

Week 6: Autoencoders

Week 6: Autoencoders

AutoEncoders. Denoise, Sparse, Variational. Generative Models. Autoregressive models.

Week 7: Generative Adversarial Models

Week 7: Generative Adversarial Models

Generative Adversarial Networks. VAE-GAN. AAE. Energy based model.

Week 8: Natural Language Processing

Week 8: Natural Language Processing

Embeddings. RNN. LSTM, GRU.

Week 9: Attention and transformer model

Week 9: Attention and transformer model

Attention Mechanism. Transformer Model.

Week 10: Transfer Learning in NLP

Week 10: Transfer Learning in NLP

Pretrained Transformers. BERT. GPT. Data Augmentation in Texts. Domain Adaptation.

Week 11: Recommender Systems

Week 11: Recommender Systems

Collaborative Filtering. FunkSVD. Neural Collaborative Filtering.

Week 12: Reinforcement Learning for RecSys

Week 12: Reinforcement Learning for RecSys

Reinforcement Learning. DQN Algorithm. DDPG Algorithm. Wolpertinger.

Week 13: Extras

Week 13: Extras

Research & Deploy. Config API. Reaction.

Recent Posts

Projects

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

Pipeine for Image Super-Resolution task that based on a frequently cited paper, ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks (Wang Xintao et al.), published in 2018.

Neuro

Brain image analysis with Catalyst

Pytorch-toolbelt

Fast R&D prototyping and Kaggle farming.

Bert Distillation

Bert Distillation with Catalyst.

GAN

Generative Adversarial Networks with Catalyst

Codestyle

Accelerated Python code formatter

Alchemy

Alchemy

Experiments logging & visualization

Reaction

Reaction

Convenient deep learning models serving

Image segmentation

Image segmentation with Catalyst

Object detection

Object detection with Catalyst

Image classification

Image classification with Catalyst

Catalyst.RL

Catalyst.RL

Catalyst.RL: A Distributed Framework for Reproducible RL Research

Catalyst

Catalyst

Accelerated deep learning research and development

Meet the Team

Core

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Dmytro Doroshenko

Pipeline master

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Yauheni Kachan

CV magician

Metric Learning

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Aleksei Shabanov

PhD student

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Julia Shenshina

ML engineer

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