Basic Concepts of Neural Network

What is Neural Networking?

A neural network is a combination of complex algorithms that tries and works similarly to a human brain to recognize underlying complex relationships and patterns in data sets. They work in a network of neurons that quickly adapt to the changing input and provide the best possible output value without requiring redevelopment of the algorithm. A neural network consists of neurons, which in this context a mathematical function that collects and classifies according to the specific architecture.

A neural network is made of layers of connected nodes. Each node works in a similar method as multiple linear regression. Nodes are perceptrons. Perceptrons use data of multiple linear regressions into a function that may be nonlinear.

Theories of Neural Network

Neural networks try to mimic the human brain with neurons (perceptrons). Artificial intelligence neural networks are inspired by the distributed representational theory of the brain. The theory states that neurons are very simple and representations of complex objects that are distributed across many neurons. Neural networks are related to regression analysis and curve fitting.

Different types of neural network

– Artificial Neural Network ( ANN )
– Convolutional Neural Network ( CNN )
– Recurrent Neural Networks ( RNN )

Artificial Neural Network (ANN)

An artificial neural network (ANN) is made of three layers: input, hidden, and output. Each layer has a set of neurons. ANN is considered a Feed-Forward neural network since it only processes input forward. Each layer does its work by trying to work out the best possible weight for the data set.

Types of ANN problem:

1. Text Data
2. Tabular Data
3. Image Data

Recurrent Neural Network

A recurrent neural network is a type of neural network that uses its output and feeds it in the input to predict the output layer. Recurrent neural networks were created to resolve issues in a feed-forward neural network.

Use cases:

1. Image Captioning
2. Natural Processing Language ( NLP )
3. Machine Translation

Types of Recurrent Neural Network

1. One to One
2. One to Many
3. Many to One
4. Many to Many

Convolutional Neural Network

Convolution neural networks are used mainly in image and video processing. They are built on filters known as kernels. These kernels extract important features of the input.

Use cases:

1. Image Tagging
2. Recommender Engines

Common use cases of Neural Network in businesses

– Improve Marketing Strategies
– Reducing Email Fatigue
– Improving search engine functionality
– Developing Personalized treatment plans
– Forecasting Market Movements
– Improving Banking operations
– Identifying credit risk

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