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!
Course
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 Networks
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.
Recent Posts
Catalyst.Neuro: A 3D Brain Segmentation Pipeline for MRI
Catalyst — A PyTorch Framework for Accelerated Deep Learning R&D
Representation Learning and Faces
Recent & Upcoming Talks
Projects
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
Experiments logging & visualization
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: A Distributed Framework for Reproducible RL Research
Catalyst
Accelerated deep learning research and development