Supervised Learning - Classification Week 9 Challenge

1.What is a Neural Network?
->Neural Network models the relationship between a set of input signals and an output signal using a model derived from our
understanding of how a biological brain responds to stimuli from sensory inputs just as a brain uses a network of
interconnected cells called neurons to create a massive parallel processor.

2.What is deep learning?
->Deep learning is a subset of machine learning which is essentially a neural network with three or more layers.These neural
network attempt to simulate the behavior of the human brain-albeit far from matching its ability allowing it to learn
from large amounts of data.

3.What is the role of the activation function?
->An activation function in a neural network defines how the weighted sum of the input is transformed into an ouput from a
node or nodes in a layes of the network.

4.What are the different types of activation functions?
->1.Rectified linear activation(ReLU)
2.Logistic(Sigmoid)
3.Hyperbolic tangent(Tanh)

5.What do you understand by backpropagation?
->Backpropagation is an important mathematical tool for improving the accuracy of predictions in data mining and machine
learning Essentially backpropagation is an algorithm used to calculate derivatives quickly.