### Valerio Maggio: _PostDoc Data Scientist @ FBK/MPBA_ ### Contacts:

@leriomaggio

vmaggio@fbk.eu

### Library Versions :::python import keras print('keras: ', keras.__version__) # optional import theano print('Theano: ', theano.__version__) import tensorflow as tf print('Tensorflow: ', tf.__version__) keras: 2.0.2 Theano: 0.9.0 Tensorflow: 1.0.1 ### Goal - Introduce main features of Keras APIs to build Neural Networks. - Learn how to implement simple and complex Deep Neural Networks Architectures using Keras. - Discover Keras Implementation and Internals. - Note: examples and hands-on exercises will be provided along the way. ### Outline in Ten (\~ish) Notebooks 1. Multi-layer Fully Connected Networks (and the backends) 2. Hidden Layers features and Embeddings 3. Convolutional Networks 4. Hyperparameter Tuning 5. Cutsom Layers 6. Deep CNN and Residual Networks 7. Transfer Learning and Fine Tuning 8. Recursive Neural Networks 9. AutoEncoders 10. Multi-Modal Networks