Supervised Learning - Classification Week 9 Challenge

1)

A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy.

2)

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks It involves more layers in between input and output.

3)

The primary role of the Activation Function is to transform the summed weighted input from the node into an output value to be fed to the next hidden layer or as output. The role of the Activation Function is to derive output from a set of input values fed to a node (or a layer).

4)

Binary step function

linear activation function

non- linear(sigmoid,tanh,ReLU,ELU,GELU,SELU,Swish)

5)

Backpropagation, or backward propagation of errors, is an algorithm that is designed to test for errors working back from output nodes to input nodes. It is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning. It simplifies the network structure by elements weighted links that have the least effect on the trained network.