Supervised Learning - Classification Week 9 Challenge

1.neural network:

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria.

2.Deep learning:

Deep Learning (sometimes called Deep Structured Learning) is a type of machine learning algorithm based on Artificial Neural Network technology (ANN).Deep learning and other ANN methods allow computers to learn by example in a similar way to the human brain. This is accomplished through passing input data through multiple levels of Neural Net processing to transform data and narrow the possible predictions each step along the way

3.Definition of activation function:- Activation function decides, whether a neuron should be activated or not by calculating weighted sum and further adding bias with it. The purpose of the activation function is to introduce non-linearity into the output of a neuron.

Explanation :-
We know, neural network has neurons that work in correspondence of weight, bias and their respective activation function. In a neural network, we would update the weights and biases of the neurons on the basis of the error at the output. This process is known as back-propagation. Activation functions make the back-propagation possible since the gradients are supplied along with the error to update the weights and biases.

4. different types of activation funciton:

• sigmoid function,
• Hyperbolic Tangent function,
• Softmax Function
• Softsign Function
• Rectified Linear Unit (ReLU) Function
• Exponential Linear Units (ELUs) Function

5. backpropagation:

Backpropagation in neural network is a short form for “backward propagation of errors.” It is a standard method of training artificial neural networks. This method helps calculate the gradient of a loss function with respect to all the weights in the network.