Speaker
Giuseppe Angora
(Istituto Nazionale di Astrofisica (INAF))
Description
1) Theoretical introduction to Supervised Learning key concepts:
- Perceptron
- Multi Layer Perceptron
- Activation Functions
- Cost Functions
- Optmizers
- Regularization techniques
- Convolutional Neural Network
- Convolution
- Pooling
- building CNNs
- CNN examples: VGG, ResNet, Inception
- Convolutional Autoencoder
The lesson includes code examples
During the hands-on session, several exercises will be carried out independently, consisting in the implementation of convolutional neural architectures.