Keras Tutorial: Deep Learning in Python

Would you like to take a course on Keras and deep learning in Python? Consider taking DataCamp's Deep Learning in Python course! Also, don't miss our Keras cheat sheet, which shows you the six steps that you need to go through to build neural networks in Python with code examples! Before going deeper into Keras and how you can use it to get started with deep learning in Python, you should probably know a thing or two about neural networks. As you briefly read in the previous section, neural networks found their inspiration and biology, where the term "neural network" can also be used for neurons. The human brain is then an example of such a neural network, which is composed of a number of neurons. And, as you all know, the brain is capable of performing quite complex computations and this is where the inspiration for Artificial Neural Networks comes from. The network a whole is a powerful modeling tool. The most simple neural network is the "perceptron", which, in its simplest form, consists of a single neuron. Much like biological neurons, which have dendrites and axons, the single artificial neuron is a simple tree structure which has input nodes and a single output node, which is connected to each input node. As you can see from the picture, there are six components to artificial neurons. This result will be the input for a transfer or activation function. In the simplest but trivial case, this transfer function would be an identity function, \(f(x) x\) or \(y x\).