Neural networks are inspired by the brain. The model has many neurons (often called nodes). We don’t need to go into the details of biology to understand neural networks.

Like a brain, neural networks can “learn”. Instead of learning, the term “training” is used. If training is completed, the system can make predictions (classifications).

Related Course: Tensorflow with Python

Introduction The neural network has: an input layer , hidden layers and an output layer . Each layer has a number of nodes.

The nodes are connected and there is a set of weights and biases between each layer (W and b).

There’s also an activation function for each hidden layer, σ . You can use the sigmoid activation function.

When couting the layers of a network, the input layer is often not counted. If we say 2-layer neural network, it means th ere are 3 layers.

To explain better, we’ll add some sample code in this tutorial.

In code:

class NeuralNetwork : def __init__ (self, x, y) : self.input = x self.weights1 = np.random.rand(self.input.shape[1 ],4 ) self.weights2 = np.random.rand(4 ,1 ) self.y = y self.output = np.zeros(y.shape)

Layers The layers are connected. The first layer is made with the input layer and weights. In this case the output layer is made with layer1 and weights2.

For a 2 layer neural network, you can have this:

def feedforward (self) : self.layer1 = sigmoid(np.dot(self.input, self.weights1)) self.output = sigmoid(np.dot(self.layer1, self.weights2))

Training Remember we said neural networks have a training process?

The training process has multiple iterations. Each iteration

calculate the predicted output y (feedforward ) updates the weights and biases (backpropagation ) During the feedforward propagation process (see code above), it uses the weights to predict the output. But what is a good output?

To find out, you need a loss function (frequently called cost function). There are many loss functions.

The loss function will be used to update the weights and biases . It’s part of the backpropagation process.