| Class |
Tutorial |
| Monday 12:00 pm - 12:55 pm | |
| Tuesday 10:00 am -10:55 am | Tuesday 10:00 am -10:55 am |
| Thursday 8:00 am - 9:00 am |
| Sl |
Date |
Assignment |
Due Date |
Files |
| 1 |
11.1.2018. |
Programming Assignment 1 - Numpy |
18.1.2018. |
|
| 2 |
18.1.2018 |
Programming
Assignment 2 - Cloth Classification |
25.1.2018. extended to |
|
| Sl | Date | Topic | Reference |
| 1 |
8.1.2018 |
Introduction |
Slides |
| 2 |
9.1.2008 |
ML Basics, Capacity |
Slides |
| 3 |
11.1.2008 |
Error, Regularization, MLE |
Slides |
| 4 |
15.1.2018 | Gradient, Loss Function |
Slides |
| 5 |
16.1.2018 | Stochastic Gradient Descent, Types of Units |
Slides |
| 6 |
18.1.2018 | Backpropagation |
Slides |
| 7 |
22.1.2018 | Preprocessing, regularization, training |
Slides |
| 8 |
23.1.2018 | Hyperparameter, gradient,
momentum, learning rate |
Slides |
| 9 |
25.1.2018 | Convolutional Neural Network 1 |
Slides |
| 10 |
29.1.2018 | Convolutional Neural Network 2 |
Slides |
| 11 |
1.2.18 |
Convolutional Neural Network 3 |
Slides |
| 12 |
5.2.18. |
Recurrent Neural Network 1 |
Slides |
| 13 |
6.2.18. |
Recurrent Neural Network 2 |
Slides |
| 14 |
8.2.18. |
Recurrent Neural Network 3 |
Slides |
| 15 |
12.2.18. |
LSTM |
Slides |
| 16 |
13.2.18. |
Word Embeddings |
Slides |
| Attention Models 1 |
Slides |
||
| Midterm Examination |
|||
| 5.3.18. to 15.3.18 |
Reinforcement learning |
RL-1 RL-2 RL-3 RL-4 CNN-DeepQ |
|
| 19.3.18. |
Deep RL applied to Alpha Go Alpha Zero |
Alpha-Go Alpha-zero |
|
| 20.3.18. |
Linear Factor Models |
LFM |
|
| 22.3.18 26.3.18 |
Autoencoders and Representations |
Autoenc |
|
| 27.3.18 |
Generative Models (VAE, GAN) |
GenModels |
|
| 2.4.18 |
GAN |
GANtut |
|
| 3.4.18 |
Some Applications of GAN References: Generative Adversarial Text to Image Synthesis Image-to-Image Translation with Conditional Adversarial Networks |
GanApp text2image image2image |
|
| 9.4.18 10.4.18 |
NLP Google multilingual neural MT Multiple encoder/decoder |
nlp1 nlp2 gmnmt multi |