Crash-course deep learning in 3 days.
Lectures and corresponding seminars are in the ./day* folders.
day 0 morning Recap
- [ ] Lecture: Linear models, stochastic optimization, regularization
- [ ] Seminar: Adaptive optimization methods for subgradient SVM
day 0 afternoon Going deeper
- [ ] Lecture: Neural networks 101
- [ ] Seminar: theano, symbolic graphs and basic neural networks
day 0 evening Yandex/HSE introductory note
day 1 morning Vision & convolutional networks
- [ ] Lecture: Computer vision problems. Convolutional neural networks. Tips and tricks. Fine-tuning.
- [ ] Seminar: lasagne and CIFAR
day 1 afternoon Natural language processing & embeddings
- [ ] Lecture: NLP problems and applications, bag of words, word embeddings, word2vec, text convolution.
- [ ] Seminar: Embeddings and text convolutions for salary prediction.
day 1 evening Generative adversarial networks
day 2 morning Recurrent neural networks for sequences
- [ ] Lecture: Sequence modelling. Simple RNN. BPTT. Gradient vanishing/explosion. LSTM/GRU. Grad clipping.
- [ ] Seminar: Generating names & modecules with recurrent neural networks
day 2 afternoon Connecting it all together: image captioning
- [ ] Lecture: RNN as Encoder/Decoder/Seq2seq. Common semantic space. Image captioning.
- [ ] Seminar: Image captioning
day 2 evening Outro
Contributors & course staff
The course is based on our HSE_deepelarning track.