Supervised Learning - Classification Week 9 Challenge
- What is a Neural Network?
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.
2.What is deep learning?
Deep learning networks learn by discovering intricate structures in the data they experience. By building computational models that are composed of multiple processing layers, the networks can create multiple levels of abstraction to represent the data.
3.What is the role of the activation function?
The activation function decides whether a neuron should be activated or not by calculating the weighted sum and further adding bias to it. The purpose of the activation function is to introduce non-linearity into the output of a neuron
4.What are the different types of activation functions
There are perhaps three activation functions you may want to consider for use in hidden layers; they are: Rectified Linear Activation (ReLU) Logistic (Sigmoid) Hyperbolic Tangent (Tanh)
5.What do you understand by backpropagation?
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