Neural Network

One of the greatest limiting factors for training effective deep learning frameworks is the availability, quality and organisation of the training data. To be good at classification tasks, we need to show our CNNs etc. as many examples as we possibly can. However, this is not always possible especially in situations where the training data is hard to collect e.g. medical image data. In this post, we will learn how to apply data augmentation strategies to n-Dimensional images get the most of our limited number of examples.

We’ve looked at the principles behind how a CNN works, but how do we actually implement this in Python? This tutorial will look at the basic idea behind Google’s TensorFlow: an efficient way to build a CNN using purpose-build Python libraries.

This series will give some background to CNNs, their architecture, coding and tuning. In particular, this tutorial covers some of the background to CNNs and Deep Learning. We won’t go over any coding in this session, but that will come in the next one. What’s the big deal about CNNs? What do they look like? Why do they work? Find out in this tutorial.

The 5th installment of our tutorial on implementing a neural network (NN) in Python. By the end of this tutorial, our NN should perform much more efficiently giving good results with fewer iterations. We will do this by implementing “momentum” into our network. We will also put in the other transfer functions for each layer.

Part 4 of our tutorial series on Simple Neural Networks. We’re ready to write our Python script! Having gone through the maths, vectorisation and activation functions, we’re now ready to put it all together and write it up. By the end of this tutorial, you will have a working NN in Python, using only numpy, which can be used to learn the output of logic gates (e.g. XOR)

The third in our series of tutorials on Simple Neural Networks. This time, we’re looking a bit deeper into the maths, specifically focusing on vectorisation. This is an important step before we can translate our maths in a functioning script in Python.

We’re going to write a little bit of Python in this tutorial on Simple Neural Networks (Part 2). It will focus on the different types of activation (or transfer) functions, their properties and how to write each of them (and their derivatives) in Python.

This is the first part of a series of tutorials on Simple Neural Networks (NN). Tutorials on neural networks (NN) can be found all over the internet. Though many of them are the same, each is written (or recorded) slightly differently. This means that I always feel like I learn something new or get a better understanding of things with every tutorial I see. I’d like to make this tutorial as clear as I can, so sometimes the maths may be simplistic, but hopefully it’ll give you a good unserstanding of what’s going on. Please let me know if any of the notation is incorrect or there are any mistakes - either comment or use the contact page on the left.