Speaker
Andrey Ustyuzhanin
(Yandex School of Data Analysis (RU))
Description
Modern neural network architecture reflects the complexity of the problem. So those may become quite complex and computationally heavy. Usually, there are plenty of different meta-parameters to tune: number of layers, activation function, number of neurons per layer, drop-out rate, etc. There many different methods and tools that aimed at tuning those parameters for various reasons - accuracy, memory footprint or inference rate. This mini-course will cover the basics approaches for neural networks optimizing including hyperparameter optimization, network architecture search and Bayesian Neural Network perspective. Practical hands-on sessions will follow the theoretical introduction.
