This is a seminar organized by 2prime© at PKU. Our target is gain basic knowledge about deep learning and be able to conqure simple computer vision tasks.
However this is not only a technical course, our focus is the mathmatics behind deep learning. At the same time, we will also learn about the optimization method used in deep learning like Adam and other stochastic optimization methods.
|TextBook||Deep Learning Book link|
|The Elements of Statistical Learning:Data Mining, Inference, and Prediction. link|
|Topics||Machine Learning Elements|
|Deep Learning & Neural Networks|
|Semi-supervised Learning and Unsupervised problems|
|Stochastic Optimization& Randomrized Numerical Linear Algebra.|
|Sparse optimization & Compressed Sensing|
Micheal I. Jordan's Advice: link
You can consider this homework as an enterance test of our seminar.You can get a Pdf version at link
Deep residual networks construced by Kaiming He wins the champion of ImageNet challange at 2015. It conqures the difficulty of training will the network becomes deep. It is a hot and interesting topic, this discussion will be hosted by Yiping Lu(SMS).
|link||Deep Residual Learning for Image Recognition|
|link||Identity Mappings in Deep Residual Networks|
|link||Learing identity mappings with residual gates|
|link||Aggregated Residual Transformations for Deep Neural Networks:code|