Deep Learning Summer School 2017 @ University of Tehran, Iran
| Session | Topics | Slides | Additional Resources | Codes |
|---|---|---|---|---|
| 1 | Linear Classifiers, Optimization in Neural Networks, Loss Function, Introduction to Deep Learning | Slide1 | ||
| 2 | Backpropagation, Introduction to TensorFlow/Pytorch | Slide2 | 1. PyTorch vs TensorFlow 2. PSET1 | Slide Examples |
| 3 | Convolutional Neural Network (CNN), CNN Layers (Pooling, Conv, Relu, Sigmoid, Tanh), A Simple CNN on MNIST Dataset | Slide3 | ||
| 4 | Regularization Technique, Dropout, Weight Initialization ,Batch Normalization | Slide4 | PSET2 | Slide Examples |
| 5 | AlexNet, VGG, GoogleNet, ResNet, Ensemble Classifiers, Data Augmentation, Transfer Learning, Pretrained Model in Software | Slide5 | ||
| 6 | Introduction to sequence data, Recurrent Neural Networks (RNN), Backpropagation through time, Introduction to Sequencial Data (Video, Speech Recognition) | Slide6 | Slide Examples | |
| 7 | Word embedding (NLP), Long Short-Term Memory(LSTM), Sentiment Analysis, Language Modeling, Language Translation, Image Captioning | Slide7 | PSET3 | Slide Examples |
| 8 | TensorFlow data input | |||
| 9 | Advanced Deep Learning Topics/Invited Talk | |||
| 10 | Advanced Deep Learning Topics/Invited Talk |
Slides and codes will be uploaded during the course.
Instructors:
Sajjad Mozaffari ([email protected])
Mohammad Mahdi Derakhshani
Official Page: http://acm.ut.ac.ir/acm/posts/224