Today to conclude my series on neural network I am going to write down some guidelines and methodology for developing, testing and debugging a neural network. As we will see (or as you already experienced) implementing a neural network is tricky and there is often a thin line between failure and success – between something […]

# Category archives: Machine learning

## Recurrent Neural Network

After introducing the convolutional neural networks I continue my serie on neural networks with another kind of specialised network: the recurrent neural network. Principle The recurrent neural network is a kind of neural network that specialises in sequential input data. With traditional neural network sequential data (e.g. time series) are split into fixed-sized windows and only the data […]

## Convolutional Neural Network

Inspiration Convolutional Neural Networks are a kind of network inspired by the cats’ visual cortex. A cat visual cortex is made of 2 distinct type of cells: simple cells which specializes into edge detection. complex cells with larger receptive field which are sensitive to a small region of the visual field and are less sensitive to the exact […]

## Keras – Tensorflow and Theano abstraction

As we’ve seen in the Tensorflow introduction having access to the computation is a powerful feature. We can define any operation we’d like and tensor flow (or Theano) will compute the gradient and perform the optimisation for us. That’s great! However if you always define the same kind of operation you’ll eventually find this approach a […]

## Neural network design

Today I continue my neural network post series with some considerations on neural network implementation. So far we covered what is a neural network and how it works but we are still left with numerous choices regarding its design. How many layers should we use, how many units (neurons) in each layer, which activation functions, […]

## Tensorflow introduction

Following my previous post on neural network I thought it would be nice to see how to implement these concepts with tensorflow. Tensor flow is a new library developed by google. It is aimed at building fast and efficient machine learning pipelines. Actually it is based on the computation graph that we discussed earlier. It provides […]

## Neural Network

Machine learning applications widespread every day in many domains. One of today’s most powerful techniques is the neural network. This technique is employed in many applications such as image recognition, speech analysis and translation, self-driving cars, etc… In fact such learning algorithms have been known for decades. But only recently it has become mainstream supported by […]

## k-means clustering

k-means is a clustering algorithm which divides space into k different clusters. Each cluster is represented by its centre of mass (i.e. barycentre) and data points are assigned to the cluster with the nearest barycentre. Algorithm The learning algorithm starts by choosing k random points. Each of these is the centre of mass of a […]

## Confusion matrix

When you train several models over a dataset you need a way to compare the model performances and choose the one that best suites your needs. As we will see there are different ways to compare the results and then pick the best one. Let’s start with what scores we can get out of the training […]

## k-Nearest Neighbours

The k-Nearest Neighbours is based on a simple idea: similar points tend to have similar outcomes. Therefore the idea is to memorise all the points in the dataset. The prediction for a new entry is made by finding the closest point in the dataset. Then the prediction for the new entry is simply the same outcome as the […]