Hi, this is Damodran. Let's see the overview of neural networks by two ways. Why do we go for neural networks and how the neural networks actually works? Why do we go for neural networks? It's very simple. Let's take a graph, x-axis of the amount of data and y-axis of the performance. Traditional machine learning algorithms like SVM, Logistic Recreation and many more algorithms is increases with the performance with a certain amount of data, but at some point it will become saturated. Even though the amount of data increases, the performance will get saturated. But the neural networks, the performance is directly proportional to the amount of data with high computational purposes. That is why we are going for the neural networks than traditional machine learning algorithms. How the neural networks actually work is based on our biological brain. Our biological brain consists of 100 billions of neurons connected with each other. Each neuron consists of a nucleus under dendrites and axones. When I touch you, you are no cells stimulus the dendrites and the dendrites connected to the neuron will produce an optical activity spike to the axon. This axon is connected to the sum of the dendrites and somehow this all neurons will connected with each other to recognize that someone touched you. This is how our biological brain's neurons works. Artificial neural networks is inspired by this by a perceptron. Artificial neural network has an input layer, output layer and the hidden layer. When we give the input feature to the input layer, the flow of data to the output layer by calculating the weights, biases and the activated neurons. Then we need to calculate the cost functions, loss values. We need to do the gradient of the descent operations by back propagation through multiple epochs or iterations. This is how the neural networks generally works. It all depends on the neurons. Activation functions weights, biases. It's all highly inspired from the human brain. Artificial neural networks has a wide range of applications like convolution neural networks, recurrent neural networks, convolution neural networks has an impact on the computer vision. Recurrent neural networks has an impact on natural language processing. Like computer vision, if a telephoto-outtaic computer vision, object detection, phase recognition, image diagnosis, that is medical image diagnosis, satellite image research. If a telephoto-outtaic computer vision, the neural networks are able to work with the neural networks. This is how artificial neural networks are giving the power to AI rather than machine learning algorithms. This is the overview of neural networks.