Credits
Online Resources
We have used huge amount of online resources for this course. All of them are the sole copyright holders of their material. Here we have refernced them with proper credits.
Students
All the TA's along with the crediting students helped alot to organize and structure this course.
Lecture Material
Study Material
| Sr. No | Lecture Topic | Slides/Resources Link | Reference Material |
|---|---|---|---|
| 1 | Course Outline | | |
| 2 | Function Approximation | | |
| 3 | Introduction to Deep Learning and Neural Networks | | |
| 4 | Linear Model Regression | | |
| 5 | How Does Deep Learning Work | | |
| 6 | Perceptron | | |
| 7 | Back Propagation | | |
| 8 | Cost Function | | |
| 9 | Convolutional Neural Network | | |
| 10 | Training Neural Networks | | |
| 11 | RCNN | | |
| 11 | YOLO: You Only Look Once | | |
| 11 | Single Shot Mutibox Detector | | |
| 12 | SegNet | | |
| 13 | Autoencoder | | |
| 14 | GAN's | | |
| 15 | Understanding Neural Networks Through Deep Visualization | | |
| 16 | Deep Convolutional Features For Iris Recognition | | |
| 17 | DeepFace and FaceNet | | |
| 18 | Host load prediction with (LSTM) long short-term memory in cloud computing | | |
| 19 | Deep-TEN | |
|
| 20 | Feature Transfer in CNN | | |
| 21 | Pixel Recurrent Neural Networks | | |
| 22 | 3D Convolutional Networks | | |
| 23 | End-to-End Spatial Transform Face Detection and Recognition | |