Supervised Learning - Classification Week 9 Challenge
Q1)What is a Neural Network?
ANS:
Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of Machine Learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
Q2)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.
Q3)What is the role of the activation function?
ANS:
Activation function decides, whether a neuron should be activated or not by calculating weighted sum and further adding bias with it. The purpose of the activation function is to introduce non-linearity into the output of a neuron.
We know, neural network has neurons that work in correspondence of weight, bias and their respective activation function. In a neural network, we would update the weights and biases of the neurons on the basis of the error at the output. This process is known as back-propagation. Activation functions make the back-propagation possible since the gradients are supplied along with the error to update the weights and biases
Q4)What are the different types of activation functions?
ANS:
- Binary Step function
- Linear function
- Non-Linear function
Binary Step function:
In simple terms, Binary functions will activate the neuron only if the input to the step function is above the threshold value; otherwise deactivates its output is not considered for the next hidden layer.
Linear function:
Equation: Linear function has the equation similar to the equation of a straight line
i.e. y = ax
Linear activation is proportional to the input. The variable ‘a’ is any constant value.
Linear Activation Functions have one advantage over binary activation. The gradient of this step function won’t be zero but the derivative of the step function won’t have ‘x’. Hence, only bias will be updated in backpropagation but weights will not be updated.
Non-Linear Activation Functions:
Practical real-life problems are often nonlinear in nature. Therefore, these problems cannot be solved without non-linear functions. Non-Linear Activation Functions are essential to introduce the non-linearity into the network. Thus helps the neural networks to learn and model non-linear and complex data, such as images, video, audio, and high dimensionality datasets.
Q5)What do you understand by backpropagation?
ANS:
A neural network can be thought of as a group of connected input/output (I/0) nodes. The level of accuracy each node produces is expressed as a loss function (error rate). Backpropagation calculates the mathematical gradient of a loss function with respect to the other weights in the neural network. The calculations are then used to give artificial network nodes with high error rates less weight than nodes with lower error rates.