The use of Keras with Tensor Flow applied to neural models and data analysis
C. Jarne. Universidad Nacional de Quilmes Departamento de Ciencia y Tecnología.
(Notebooks and talks available at the bottom of this site with the video also)
Description of the tutorial
This tutorial will help participants implement and explore simple neural models using Keras  as well as the implementation of neural networks to apply Deep learning tools for data analysis. It will include an introduction to modeling and hands-on exercises. The tutorial will focus on using Keras, which is an open-source framework to develop Neural Networks for rapid prototyping and simulation with TensorFlow  as backend. The tutorial will show how models can be built and explored using python. The hands-on exercises will demonstrate how Keras can be used to rapidly explore the dynamics of the network.
Keras is a framework that greatly simplifies the design and implementations of Neural Networks of many kinds (Regular classifiers, Convolutional Neural Networks, LSTM, among others). In this mini-course, we will study implementations of neural networks with Keras. It is split into two sections: On one side, we will introduce the main features of Keras, showcasing some examples, and then, we will do a set of two guided on-line hands-on with exercises to strengthen the knowledge.
For this tutorial, you will need basic knowledge of NumPy, SciPy, and matplotlib. To be able to carry out the tutorial, students need a laptop with Linux and these libraries installed:
- Scikit learn
I recommend the following sites where is explained the installation of following packages that include a set of the named libraries and some additional tools:
 Francois Chollet et al. Keras. https://keras.io, 2015.
 Martín Abadi, et al. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.
 Deep Learning And Reinforcement Summer School (DLRLSS): https://dlrlsummerschool.ca/past-years/
 Deep Learning with Keras. Antonio Gulli Sujit Pal. Packt Publishing Ltd. 2017.