Hands-On Ensemble Learning with Python

Neural networks

Neural networks, inspired by the way biological brains are connected, consist of many neurons, or computational modules, organized in layers. Data is provided at the input layer and predictions are produced at the output layer. All intermediate layers are called hidden layers. Neurons that belong to the same layer are not connected to each other, only to neurons that belong in other layers. Each neuron can have multiple inputs, where each input is multiplied by a specific weight and the sum of multiplied inputs is passed to an activation function that defines the neuron's output. Common activation functions include the following:

Sigmoid	Tanh	ReLU	Linear

The network's goal is to optimize each neuron's weights, ...