Supervised Learning - Classification Week 9 Challenge

Q1.

Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns
and solve common problems in the fields of AI, machine learning, and deep learning.

Q2.

Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge.
Deep learning is an important element of data science, which includes statistics and predictive modeling.

Q3.

The role of activation functions is make neural networks non-linear.
Simply put, an activation function is a function that is added into an artificial neural network
in order to help the network learn complex patterns in the data. When comparing with a neuron-based model that is in our brains,
the activation function is at the end deciding what is to be fired to the next neuron.
That is exactly what an activation function does in an ANN as well.
It takes in the output signal from the previous cell and converts it into some form that can be taken as input to the next cell.

Q4.

Sigmoid Function.
Hyperbolic Tangent Function (Tanh)
Softmax Function.
Softsign Function.
Rectified Linear Unit (ReLU) Function.
Exponential Linear Units (ELUs) Function.

Q5.

Backpropagation, or backward propagation of errors, is an algorithm that is designed to test for errors working back from output nodesto input nodes.
It is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning.