Supervised Learning - Classification Week 9 Challenge

Question1. 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.

Question2.What is deep learning?

Deep learning is a subset of machine learning which provides the ability to machine to perform human-like tasks without human involvement. It provides the ability to an AI agent to mimic the human brain. Deep learning can use both supervised and unsupervised learning to train an AI agent. Deep learning is implemented through neural networks architecture hence also called a deep neural network. Deep learning is the primary technology behind self-driving cars, speech recognition, image recognition, automatic machine translation, etc. The main challenge for deep learning is that it requires lots of data with lots of computational power.

Question3. What is the role of the activation function?

The activation function compares the input value to a threshold value. If the input value is greater than the threshold value, the neuron is activated. It's disabled if the input value is less than the threshold value, which means its output isn't sent on to the next or hidden layer.

Question4. What are the different types of activation functions?

1. Popular types of activation functions
   1. Binary Step
   2. Linear
   3. Sigmoid
   4. Tanh
   5. ReLU
   6. Leaky ReLU
   7. Parameterised ReLU
   8. Exponential Linear Unit
   9. Swish
   10. Softmax

Question5. What do you understand by backpropagation?

Back-propagation is just a way of propagating the total loss back into the neural network to know how much of the loss every node is responsible for, and subsequently updating the weights in such a way that minimizes the loss by giving the nodes with higher error rates lower weights and vice versa.