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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep Learning with Catalyst
| Date | Stars |
|---|---|
| 2026-07-31 | 298 |
| 2026-08-03 | 298 |
| 2026-08-06 | 298 |
Today
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growth rate 0.00%/day
# Deep Learning with Catalyst [](https://stepik.org/course/83344/syllabus) [](https://join.slack.com/t/catalyst-team-devs/shared_invite/zt-d9miirnn-z86oKDzFMKlMG4fgFdZafw) [](https://github.com/catalyst-team/dl-course) This is an open deep learning course made by [Deep Learning School](https://dlschool.org), [Tinkoff](https://tinkoff.ru), and [Catalyst team](https://github.com/catalyst-team). Lectures and practice notebooks located in ```./week*``` folders. Homeworks are in ```./homework*``` folders. > *Note: the course is under update: > weeks with colab barge are ready to go, weeks with [WIP] label are still in progress. > You could use the `v20.12` branch for the earlier version of the full course.* ## Syllabus - [](https://colab.research.google.com/github/catalyst-team/dl-course/blob/master/week-01/seminar.ipynb) week 1: Deep learning intro - Deep learning – introduction, backpropagation algorithm. Optimization methods. - Neural Network in numpy. - [](https://colab.research.google.com/github/catalyst-team/dl-course/blob/master/week-02/seminar.ipynb) week 2: Deep learning frameworks - Regularization methods and deep learning frameworks. - Pytorch basics & extras. - [](https://colab.research.google.com/github/catalyst-team/dl-course/blob/master/week-03/seminar.ipynb) week 3: Convolutional Neural Network - CNN. Model Zoo. - Convolutional kernels. ResNet. Simple Noise Attack. - [](https://colab.research.google.com/github/catalyst-team/dl-course/blob/master/week-04/seminar_done.ipynb) week 4: Object Detection, Image Segmentation - Object Detection. (One, Two)-Stage methods. Anchors. - Image Segmentation. Up-scaling. FCN, U-net, FPN. DeepMask. - [](https://colab.research.google.com/github/catalyst-team/dl-course/blob/master/week-05/seminar_done.ipynb) week 5: Metric Learning - Metric Learning. Contrastive and Triplet Loss. Samplers. - Cross Entropy Loss modifications. SphereFace, CosFace, ArcFace. - [](https://colab.research.google.com/github/catalyst-team/dl-course/blob/master/week-06/seminar_done.ipynb) week 6: Autoencoders - AutoEncoders. Denoise, Sparse, Variational. - Generative Models. Autoregressive models. - [](https://colab.research.google.com/github/catalyst-team/dl-course/blob/master/week-07/seminar_done.ipynb) week 7: Generative Adversarial Models - Generative Adversarial Networks. VAE-GAN. AAE. - Energy based model. - [](https://colab.research.google.com/github/catalyst-team/dl-course/blob/master/week-08/seminar_done.ipynb) week 8: Natural Language Processing - Embeddings. - RNN. LSTM, GRU. - [](https://colab.research.google.com/github/catalyst-team/dl-course/blob/master/week-09/seminar_done.ipynb) week 9: Attention and transformer model - Attention Mechanism. - Transformer Model. - [](https://colab.research.google.com/github/catalyst-team/dl-course/blob/master/week-10/seminar_done.ipynb) week 10: Transfer Learning in NLP - Pretrained Transformers. BERT. GPT. - Data Augmentation in Texts. Domain Adaptation.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:06f7f46c317e0ec2, name:course