neural networks

In this blog post, I will learn you how you can mine opinions about companies from news articles. I will share how I scraped thousands of news articles in a few minutes and how one could classify the opinion expressed in the titles of the news articles. This information could be used for example to help with watching competitors of a company or to predict global trends.

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This tutorial is a practical guide which helps you to create Neural Networks in Chainer. The focus is not on the architecture of the networks (more about Neural Network architectures is found in this post), but it is focused on creating a pipeline. We will take a simple classification problem as an example and create the pipeline for training and testing the network and how to evaluate the model.

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In this blog post series, we will use a neural network for predicting restaurant reservations. This first post will describe how we can use a neural network for predicting the number of days between the reservation and the actual visit given a number of visitors.

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MLPs (Multi-Layer Perceptrons) are great for many classification and regression tasks. However, it is hard for MLPs to do classification and regression on sequences. In this Python deep learning tutorial, a GRU is implemented in TensorFlow. Tensorflow is one of the many Python Deep Learning libraries.

By the way, another great article on Machine Learning is this article on Machine Learning fraud detection. If you are interested in another article on RNNs, you should definitely read this article on the Elman RNN.

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In this Python Deep Learning tutorial, an implementation and explanation is given for an Elman RNN. The implementation is done in Tensorflow, which is one of the many Python Deep Learning libraries.

A more modern RNN is the GRU. A GRU has less parameters to train and is therefore quite fast. An implementation in Tensorflow of the GRU can be found here.

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