Case Study on Neural network

Mihir Patel
4 min readMar 30, 2021


What are Neural networks?

Neural networks are set of algorithms inspired by the functioning of human brain. Generally when you open your eyes, what you see is called data and is processed by the Nuorans in your brain, and recognize what is around you. That’s how similar the Neural Networks works. They takes a large set of data, process the data, and outputs what it is.

Let’s How it work ?

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.

Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.

One of the most well-known neural networks is Google’s search algorithm.

Learning Techniques in Neural Networks

Unsupervised Learning

The input data is used to train the network whose output is known. The network classifies the input data and adjusts the weight by feature extraction in input data.

Supervised Learning

In unsupervised learning, the training data is input to the network, and the desired output is known weights are adjusted until production yields desired value.

Reinforcement Learning

Here the value of the output is unknown, but the network provides feedback on whether the output is right or wrong. It is Semi-Supervised Learning.

Online Learning

The adjustment of the weight and threshold is made after presenting each training sample to the network.

Offline Learning

The adjustment of the weight vector and threshold is made only after all the training set is presented to the network. It is also called Batch Learning.

Types of Neural Network .

Feedforward Neural Network — Artificial Neuron

This is one of the simplest types of artificial neural networks. In a feedforward neural network, the data passes through the different input nodes until it reaches the output node.

In other words, data moves in only one direction from the first tier onwards until it reaches the output node. This is also known as a front propagated wave which is usually achieved by using a classifying activation function.

Feedforward neural networks are used in technologies like face recognition and computer vision. This is because the target classes in these applications are hard to classify.

Multilayer Perceptron :

A perceptron is a single-layer neural network. A multilayer perceptron has three or more layers. It is used to classify data that cannot be separated linearly. It is a type of artificial neural network that is fully connected. This is because every single node in a layer is connected to each node in the following layer.

Convolutional Neural Network :

A convolutional neural network(CNN) uses a variation of the multilayer perceptrons. A CNN contains one or more than one convolutional layers.

Before passing the result to the next layer, the convolutional layer uses a convolutional operation on the input. Due to this ability, convolutional neural networks show very effective results in image and video recognition, natural language processing, and recommender systems.

Recurrent Neural Network(RNN) :

A Recurrent Neural Network is a type of artificial neural network in which the output of a particular layer is saved and fed back to the input. This helps predict the outcome of the layer.

The first layer is formed in the same way as it is in the feedforward network. That is, with the product of the sum of the weights and features. However, in subsequent layers, the recurrent neural network process begins.

From each time-step to the next, each node will remember some information that it had in the previous time-step. In other words, each node acts as a memory cell while computing and carrying out operations. The neural network begins with the front propagation as usual but remembers the information it may need to use later .

Use Cases on Neural network :

  • Artificial Neural Networks are Improving Marketing Strategies
  • Developing Targeted Marketing Campaigns
  • Improving Search Engine Functionality
  • in the pharmaceutical industry
  • Developing Personalised Treatment Plans
  • Neural Networks in the Retail Sector

Thanks For reading ….