Ten Steps to Keras @ PyData London 2017

Deep Learning with Keras and Tensorflow

Valerio Maggio: PostDoc Data Scientist @ FBK/MPBA

Contacts:

@leriomaggio

[email protected]

Library Versions

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