Python Tutorial: Your first neural network

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-deep-learning-with-keras at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- It's time to start building some neural networks! A neural network is a machine learning algorithm with the training data being the input to the input layer and the predicted value the value at the output layer. Each connection from one neuron to another has an associated weight w. Each neuron, except the input layer which just holds the input value, also has an extra weight we call the bias weight, b. During feed-forward our input gets transformed by weight multiplications and additions at each layer, the output of each neuron can also get transformed by the application of what's called an activation function. Learning in neural networks consists of tuning the weights or parameters to give the desired output. One way of achieving this is by using the famous gradient descent algorithm, and applying weight updates incrementally via a process known as back-propagation. That was a lot of theory! The code in Keras is much simpler as we will see now. Keras allows you to build models in two different ways; using either the Functional API or the Sequential API. We will focus on the Sequential API. This is a simple, yet very powerful way of building neural networks that will get you covered for most use cases. With the sequential API you're essentially building a model as a stack of layers. You can start with an input layer. Add a couple of hidden layers. And finally end your model by adding an output layer. Let's go through a code example. To create a simple neural network we'd do the following: Import the Sequential model from Keras.models. Import a Dense layer, also known as fully connected layer, from Keras.layers. We can then create an instance of a Sequential model.In this next line of code we add two layers; a 2 neuron Dense f