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learning-deep-learning

Course program

  • Basics of deep learning

    1. Introduction, backpropagation algorithm
    2. Empirical risk minimization, standard loss functions, linear classification, stochastic optimizers

  • Computer vision

    1. Convolutional networks (ConvNets), classifying images. Homework
    2. "Deep" computer vision beyond classification: Verification tasks, object detection architectures, semantic segmentation
    3. Generation networks
      -- Article deadline: Journal club --
    1. Generation: AE, VAE, GAN. Homework

  • Natural language processing

    1. Word embeddings, word2vec and other variants, convolutional networks for natural language
    2. RNN, LSTM. Homework
    3. Sequence2sequence, attention, transformers and other advanced techniques
    -- Article deadline: present current results --

  • Deep reinfocrement learning. Homework

  • Adverserial examples, MobileNet, distillation, dark knowledge

    -- Artice deadline: present final results --

Classes

Lecture 1. (6.02.2019)

Lecture Introduction, backpropagation algorithm .pptx, .pdf

Seminar Introduction to pytorch.

Homework: Fill Seminar 1 notebook and send it to AnyTask until 13.02.19 (8:00)

Lecture 2. (13.02.2019)

Lecture Optimization for Deep Learning .pptx, .pdf.

Seminar High level pytorch.

Homework:

Lecture 3. (20.02.2010)

Lecture Convolutional Networks. .pptx, .pdf

Seminar Cifar10 finetuning.

Homework:

  • Fill Seminar 3 notebook
  • HW1 Cifar10 classification.

Lecture 4. (27.02.2019)

Lecture Convolutional Networks in Computer Vision (segmentation, detection, verification) .pptx, .pdf

Seminar Dense prediction.

Homework:

  • Fill Seminar 4 notebook
  • Find an article for journal club (13.03).

Lecture 5. (06.03.2019)

Lecture Generative Convolutional Networks .pptx, .pdf

Seminar Neural Style Transfer.

Homework:

  • Fill Seminar 5 notebook
  • Find an article for journal club (13.03).

Journal club. (13.03.2019)

Lecture 6. (20.03.2019)

Lecture Autoencoders and GANs. .pptx, .pdf

Useful link – VAE: https://neurohive.io/ru/osnovy-data-science/variacionnyj-avtojenkoder-vae/

Seminar Fashion-MNIST GAN

Homework:

  • Fill Seminar 6 notebook

Lecture 7. (27.03.2019)

Lecture NLP intro, ConvNets for NLP, Word embeddings.

Seminar w2v

Homework:

  • Fill Seminar 7 notebook

Lecture 8. (3.04.2019)

Lecture RNN, LSTM. .pptx, .pdf

Seminar Char RNN

Homework:

Lecture 9. (10.04.2019)

Lecture Speech2Text. Seq2seq. Transformer .pptx, .pdf

Seminar Seq2seq

Homework:

  • Fill Seminar 9 notebook
  • Upload intermediate results of course work

Lecture 10. (17.04.2019)

Lecture Reinforcement learning w/o NN. .pptx, .pdf

Seminar Q-learning

Homework:

  • Fill Seminar 10 notebook

Lecture 11. (24.04.2019)

Lecture Deep Reinforcement Learning. DQN, Policy gradients. .pptx, .pdf

Seminar -

Homework:

  • Project

Сourse evaluation criteria

  • 10 seminar tasks: 4 points each
  • 4 homeworks: 10 points each
  • 1 article implementation + journal club. 5 (journal club) + 5 (current results) + 10 (final results) points

Submissions missed deadlines are estimated at half points maximum.

Total sum is 100 points. Course grades:

  • 80 points -> 8/10
  • 50 points -> 5/10
  • 30 points -> 3/10