Supervised Learning - Classification Week 9 Challenge

1..........What is a Neural Network?

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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?

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Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input.

3............What is the role of the activation function?

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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...............What are the different types of activation functions?

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the diffferent types of Activation Functions are:

• Sigmoid Function.
• Hyperbolic Tangent Function.
• Rectified Linear Unit Function (ReLU).
• Softmax Function.
• Exponential linear units Function(ELUs).
• Softsign Function.

5.................What do you understand by backpropagation?

Backpropagation (backward propagation) is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning. When training a neural network by gradient descent, a Loss function is calculated, which represents how far the network's predictions are from the true labels. Backpropagation allows us to calculate the gradient of the loss function with respect to each of the weights of the network. This enables every weight to be updated individually to gradually reduce the loss function over many training iterations. Backpropagation involves the calculation of the gradient proceeding backwards through the feedforward network from the last layer through to the first. To calculate the gradient at a particular layer, the gradients of all following layers are combined via the chain rule of calculus.