Supervised Learning - Classification Week 9 Challenge

What is a Neural Network?

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so, the network generates the best possible result without needing to redesign the output criteria. Elements of a Neural Network :- Input Layer :- This layer accepts input features. It provides information from the outside world to the network, no computation is performed at this layer, nodes here just pass on the information(features) to the hidden layer. Hidden Layer :- Nodes of this layer are not exposed to the outer world, they are the part of the abstraction provided by any neural network. Hidden layer performs all sort of computation on the features entered through the input layer and transfer the result to the output layer. Output Layer :- This layer bring up the information learned by the network to the outer world.

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 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.

Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).

What is the role of the activation function?

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.

What are the different types of activation functions?

Popular types of activation functions are

Binary Step

Linear

Sigmoid

Tanh

ReLU

Leaky ReLU

Parameterised ReLU

Exponential Linear Unit

Swish

Softmax

What do you understand by backpropagation?

Backpropagation is an algorithm used in artificial intelligence (AI) to fine-tune mathematical weight functions and improve the accuracy of an artificial neural network's outputs.

A neural network can be thought of as a group of connected input/output (I/O) 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.

Backpropagation uses a methodology called chain rule to improve outputs. Basically, after each forward pass through a network, the algorithm performs a backward pass to adjust the model’s weights.

An important goal of backpropagation is to give data scientists insight into how changing a weight function will change loss functions and the overall behaviour of the neural network. The term is sometimes used as a synonym for error correction.