Mathematics and Machine Learning
Reading group
With Nicolai Baldin (Statslab)
Reading group: Underpinning techniques of most widely used DNN architectures
In the first session we will look more closely into common techniques of widely used NN architectures like batch normalisation, dropout and stochastic optimisers. We shall also touch upon regularisation ideas and various activation functions.
It will be roughly based upon the following papers:
1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift pdf
2. Dropout: A Simple Way to Prevent Neural Networks from Overfitting pdf
3. Adam: A Method for Stochastic Optimization pdf
The first session will be given by the organisers but participants are expected to be familiar with the papers. More information about the reading group can be found at mathsml.com
- Speaker: Nicolai Baldin (Statslab)
- Tuesday 31 October 2017, 14:00–15:00
- Venue: Centre for Mathematical Sciences, MR2.
- Series: Mathematics and Machine Learning; organiser: Nicolai Baldin.
