Fork of Lempitsky DL for HSE master students.

Lecture and seminar materials for each week are in ./week* folders

Attention! This is a new iteration of on-campus deeplearning course. For full course materials '2016, go to this branch

• Create cloud jupyter with repo https://beta.mybinder.org/v2/gh/yandexdataschool/Practical_DL/fall17
• Lecture slides are stored in this folder (odp, pdf). You can also view slides from each week's page.
• Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue

HSE classes are happening on wednesdays, 18-10 till 21-00. [room number TBA]

Everyone who wants to attend the course ping jheuristic@yandex-team.ru

1. Bookmark repo https://github.com/yandexdataschool/practical_DL
2. Join telegram chat https://t.me/dl_hse_fall17
3. (only HSE students) Enroll to anytask.org/course/227 with invite code 7pp6jP3
4. Join piazza https://piazza.com/cs_hse/fall2017/dl101/home with access code dl101
5. Read rules

• 21.09 - for those using TF + keras in week2: if you have any problems please update your notebooks from current repository (new data reading script).
• 06.09 - Course started

• week0 Recap

  • Lecture: Linear models, stochastic optimization, basic neural networks and backprop
  • Seminar: Neural networks in numpy, adaptive SGD
    • HW due: 17.09.17, 23.59.
  • Please get bleeding edge theano+lasagne installed for the next seminar.
    • Issue
    • Linux Guidelines
    • You may choose tensorflow/pytorch version if you prefer 'em

• week1 Symbolic graphs

  • Lecture: Backprop recap. Deep learning frameworks. Some philosophy. DL tricks: dropout, normalization
  • Seminar: Symbolic graphs and basic neural networks
    • HW due 24.09.17, 23.59

• week2 Deep learning for computer vision

  • Lecture: Convolutional neural networks, data augmentation & hacks.
  • Seminar: Convnets for CIFAR
    • HW due 1.10.17, 23.59

• week3 Advanced computer vision

  • Lecture: Computer vision beyond image classification. Segmentation, object detection, identification. Model zoo & fine-tuning
  • Seminar: Model zoo. Siamese nets for identification.
    • HW due 15.10.17, 23.59

• week4 Unsupervised & generative methods

  • Lecture: Autoencoders, Generative Adversarial Networks
  • Seminar: Generative Adversarial Networks. [hopefully] Art Style Transfer by Dmitry Ulyanov
    • HW due 29.10.17, 23.59

• week5 Deep learning for natural language processing 101

  • Lecture: NLP problems and applications, bag of words, word embeddings, word2vec, text convolution.
  • Seminar: Word embeddings. Text convolutions for salary prediction.
    • HW due 16.11.17, 23:59

• week6 Recurrent neural networks

  • Lecture: Simple RNN. Why BPTT isn't worth 4 letters. GRU/LSTM. Language modelling. Optimized softmax. Time series applications.
  • Seminar: Generating laws for pitiful humans with mighty RNNs.

• week7 Recurrent neural networks II

  • Lecture: Sequence labeling & applications. Seq2seq & applications. Attention. Batchnorm and dropout for RNN.
  • Seminar: Image Captioning

• week8: Deep embedding learning

  • Lecture: Basics of deep embedding learning, losses, sampling techinuques.
  • Seminar: Question answering system
    • HW due 26.11.17, 23:59

• week9: Bayesian deep learning

  • Lecture: Bayesian vs Frequentist idea of probability. Bayesian methods around you. Variational Autoencoder. Bayesian Neural Network.
  • Seminar: Bayesian Neural Nets; Variational autoencoders [Hopefully by Mikhail Khalman]

• One rule to rule them all
• Project rules
• Project examples
• Reducing lateness penalty
• Feedback form (anonymous)

Course materials and teaching performed by

• Fedor Ratnikov - lectures, seminars, hw checkups
• Oleg Vasilev - seminars, hw checkups, technical issue resolution
• Arseniy Ashukha - image captioning, sound processing, week7&9 lectures
• Dmitry Ulyanov - generative models, week8 lecture, week12 homework assignment
• Mikhail Khalman - variational autoencoders, lecture 12
• Vadim Lebedev - week0 & week6 homeworks