Supervised Learning - Classification Week 9 Challenge

#Question No:1 What is a Neural Network?

Ans:-Neural networks, also known as artificial neural networks Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network

## Question No:-2 What is deep learning?

Ans:-Deep learning is a subset of machine learning which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.

Deep learning drives many applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).

### Question No3 :-What is the role of the activation function?

Ans:-The activation function defines the output of a neuron / node given an input or set of input (output of multiple neurons). It’s the mimic of the stimulation of a biological neuron.

The output of the activation function to the next layer (in shallow neural network: input layer and output layer, and in deep network to the next hidden layer) is called forward propagation (information propagation). It’s considered as a non linearity transformation of a neural network.

Question No :-4 What are the different types of activation functions?

Ans:-Several different types of activation functions

I) Step Function:-Step Function is one of the simplest kind of activation functions. In this, we consider a threshold value and if the value of net input say y is greater than the threshold then the neuron is activated.

II)Sigmoid Function:it is widely used function.when I have multiple neurons having sigmoid function as their activation function – the output is non linear as well.

III)ReLU: The ReLU function is the Rectified linear unit. It is the most widely used activation function. The main advantage of using the ReLU function over other activation functions is that it does not activate all the neurons at the same time. What does this mean ? If you look at the ReLU function if the input is negative it will convert it to zero and the neuron does not get activated.

IV)Leaky ReLU: Leaky ReLU function is nothing but an improved version of the ReLU function

Question No 5:- What do you understand by backpropagation?

Ans:-Backpropagation is an algorithm used in artificial intelligence AI to fine-tune mathematical weight functions and improve the accuracy of an Artificial neural network outputs.An important goal of backpropagation is to give data scientists insight into how changing a weight function will change loss functions and the overall behaviour of the neural network