Published on March 11, 2018 by

Filmed at PyData London 2017

In this tutorial we will learn Keras in ten steps (a.k.a. Jupyter Notebooks). We will warm up by learning how to create a multi layer network, and then we will go through more sophisticated topics such as implementing different types of networks (e.g. RNN, CNN), creating custom layers and discovering Keras internals. numpy proficiency and basic knowledge of Machine/Deep Learning are assumed.

Goal of the Tutorial

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 Notebooks)
Multi-layer Fully Connected Networks (and the backends)
Bottleneck features and Embeddings
Convolutional Networks
Transfer Learning and Fine Tuning
Residual Networks
Recursive Neural Networks
[Variational] AutoEncoders and Adversarials
Multi-Modal Networks
Custom Layers and Optimisers
Interactive Networks and Callbacks
Multi-layer Fully Connected Networks In this notebook we will learn the basic building blocks of Keras APIs to create deep neural networks. We will learn how to use the Sequential object as well as Functional and keras.backend APIs. First examples of MLP and Fully Connected Networks will be presented.

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We aim to be an accessible, community-driven conference, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

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