Codes for SUTD deep learning course
| Ex | Description |
|---|---|
| lecture_4 | Handwritten SGD |
| lecture_5 | Performance evaluation of for-loop, numpy and pytorch |
| lecture_6 | Calculating gradient with pytorch autograd |
| lecture_6(2) | Handwritten neural network |
| lecture_12 | Gaussian mixtures |
| lecture_13 | Gradient ascend adverserial attack |
| lecture_16 | Foolbox adverserial attack |
| HW | Description |
|---|---|
| 1 | An implementation of 17 class-wise binary classifiers |
| 2 | Simple 2 layer neural network for Fashion MNIST dataset, usage of Python dataloader class |
| 3 | Pytorch Resnet classifier on Imagenet dataset, usage of different data augmentation methods such as resize, Fivecrop and data normalization |
| 4 | Pytorch Resnet classifier on 102 Flowers dataset, usage of transfer learning with weights from Imagenet dataset |
| 5 | Pytorch LSTM classifier on country names dataset |
| 6 | Pytorch LSTM sequence generator on Star Trek dataset, usage of sampling with temperature |
| PRJ | Description |
|---|---|
| 1 | Pytorch Resnet on PascalVOC imageset with GUI viewer, our submission achieved top 20 on global leaderboard |
| 2 | Pytorch LSTM sequence generator on Clickbait dataset to generate clickbait |