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.
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.
week 1: Deep learning intro
- Deep learning โ 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
- [WIP] week 13: Extras
- Research & Deploy.
- Config API. Reaction.
# setup - env
conda create -n catalyst-dl python=3.7 anaconda
source activate catalyst-dl
conda remove nb_conda_kernels -y
conda install -c conda-forge nb_conda_kernels -y
conda install notebook jupyter nb_conda -y
conda remove nbpresent -y
# setup - jupyter
jupyter notebook password
# jupyter run
jupyter notebook --no-browser --ip 0.0.0.0 --port 8888
pip install -U catalyst==21.04.2 torch==1.8.0 albumentations==0.5.0