Overview

This TensorFlow tutorial, held in the "25º Escuela de Verano de Ciencias Informáticas de la Universidad Nacional de Río Cuarto", focuses on practical aspects of implementing and training deep learning models. We will follow, step by step, the implementation of neural networks models with TensorFlow and its comparison with Scikit Learn, focusing on the impact of tuning hyperparameters and architectures. To keep track of results we will use the visualization tool Tensorboard and show its functionalities.

Objective

The goal of this tutorial is for you to learn how to build models in Tensorflow. You will apply them to a standard document classification dataset, and keep track of the experiments' performance with Tensorboard, a visualization tool.

Who is this course aimed to?

We strongly recommend to have covered some machine learning basic concepts previously, as we wont cover them during the tutorial:

• What is a classifier? What is a logistic regression classifier?
• Basic evaluation: metrics like accuracy, precision, and recall. What is the train and test split for?
• What is gradient descent and what is a cost function?

Technical requirements

This tutorial is based in Jupyter notebooks we uploaded to the Github platform. The notebook named as Part 0: Configuration (which can be rendered directly from Github) has all the software and data requirements needed to configure the environment. It should be followed before taking the tutorial in the conference.

Course slides

The slides shown in the tutorial presentation are available using this link.

Software

The software packages we are using are Python 3.5 (obtained through conda) along Numpy, Scipy, Jupyter, and the Tensorflow (version 1.4) libraries.

Please refer to the notebook for detailed explanation.

Dataset

The dataset we will use is a pre-processed version of the 20 Newsgroup Corpus for document classification. Part 1 of the tutorial is optional and explains how this dataset is obtained. In any case it is possible to download the dataset directly to use in Tensorflow from part 2 and after of the tutorial.

Please refer to the notebook for detailed explanation and download link.

Structure

The Jupyter notebooks of the tutorial contain code and explanations that the attendees can download, execute and modify during the talk. It will comprise the following parts:

Introduction and environment setup

This part is covered by the Part 0: Configuration notebook. It has the instruction to configure the environment and download the dataset to use.

Dataset preprocessing (optional)

This part is covered by the Part 1: Dataset Preprocessing notebook. It explains the dataset we are using to experiment with Tensorflow and how we process it to have the final version ready.

Scikit Learn

This part is covered by the Part 2: Scikit Learn notebook. It explains the implementation of a neural network using Scikit Learn API. This is explored as a comparison point to the TensorFlow approach.

TensorFlow's API

This part is covered by the Part 3: TensorFlow's API notebook. This part explores the backbone of TensorFlow and how it can be used to create models from scratch.

TensorFlow DNNClassifier

This part is covered by the Part 4: TensorFlow DNNClassifier notebook. This explore a simpler API offered by the newer versions of TensorFlow to create a neural network model.

Using Tensorboard

This part is covered by the Part 5: Using Tensorboard notebook. It showcases the visualization tool that comes along with TensorFlow, named Tensorboard. This part works on how to visualize the progression of a neural network training.

About the authors

Milagro Teruel

I'm a Computer Scientist based in Córdoba, Argentina. I'm currently pursuing a PhD in Computing under the direction of Laura Alonso Alemany at Universidad Nacional de Córdoba and Marcelo Luis Errecalde, from the Universidad Nacional de San Luis.

Cristian Cardellino

I have a degree in Computer Sciences from Universidad Nacional de Córdoba, Argentina, and I am finishing my PhD. in the area of machine learning applied to natural language processing.