This is the final part in our series on Generative Adversarial Networks (GAN). We will write our training script and look at how to run the GAN. We will also take a look at the results we get out. Can you tell the difference between the real and generated faces?
Now that we’re able to import images into our network, we really need to build the GAN iteself. This tuorial will build the GAN class including the methods needed to create the generator and discriminator. We’ll also be looking at some of the data functions needed to make this work.
This tutorial will provide the data that we will use when training our Generative Adversarial Networks. It will also take an overview on the structure of the necessary code for creating a GAN and provide some skeleton code which we can work on in the next post. If you’re not up to speed on GANs, please do read the brief introduction in Part 1 of this series on Generative Adversarial Networks.
We’ve seen that CNNs can learn the content of an image for classification purposes, but what else can they do? This tutorial will look at the Generative Adversarial Network (GAN) which is able to learn from a set of images and create an entirely new ‘fake’ image which isn’t in the training set. Why? By the end of this tutorial you’ll get know why this might be done and how to do it.
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.
