Deep Learning tutorial in Python using Keras and Tensorboard for the 2017 "Argentinian School in Artificial Intelligence" (EAIA)
In this tutorial we aim to give you some notions on how Deep Learning algorithms are implemented and the key points to use them and not get burned in the process. We will center on how to translate the theory of neural networks easily into efficient code with as little effort as possible. We won't teach you how to implement the complex algorithms involved, but rather how to ensemble the available parts to get the classification pipeline you want. You don't need to be an expert in machine learning or deep learning to understand this content, but some prior background is required. Also, we count on you knowing how to program in Python and how to use Jupyter Notebooks.
To follow the tutorial please clone this repository and check the instructions in notebook deep_learning_tutorial_0.ipynb
that will explain how to set up your environment. Please don't hesitate to contact us with questions or feedback, it's always
welcome!
Enjoy!
Cristian and Milagro