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 […]

# Monthly archives: May 2016

## 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 […]