Neural Networks

Neural networks are one of the best programming’s ever invented in the history of humankind. In the conventional approach, we used to instruct the computer what to do. On the contrary, now we don’t tell the computer how to solve our neural network problems. Because it automatically learns from observational data and figures out a solution to the problem.

First of all, we will learn the basic definition of neural networks. A neural network is defined as a series of systems or circuits. An artificial neural network made up of artificial neurons or nodes can be termed as neural networks. Neural Networks are a category of models within machine learning. Its appearance, as well as functioning, is very much similar to a biological neural network.

These are the set of algorithms which are designed in such a way that it can recognize various machine learning patterns. In other words, it can be said that machine learning has revolutionized many ways after discovering neural networks. They help in the sensory data interpretation through machine learning. They recognize patterns in the forms of numbers, vectors, images, sound, text, etc.

You must be wondering what’s the need for studying Neural Networks. Here are the top three reasons:

  1. To understand the functioning of the brain and its complication. Because of neural networks in data science work as same as the human brain and help in understanding computer simulations.
  2. To understand parallel computation inspired by neurons and different sequential computations.
  3. To solve practical problems using algorithms.

History and Technology

In 1959, Frank Rosenblatt, who was a psychologist, invented a neural network called Perceptron. It was invented to model how the human brain processed visual data and recognize objects.

With the advancement of technology, computation has also evolved in many ways. When the first computer was designed, it wasn’t enabled with features that we are using presently. Hence, the neural networks also used their different abilities to make the whole process easy. Its pattern matching and learning capabilities allowed them to solve many problems that were difficult to explain by computational and statistical methods.

Components of Neural Networks

Neural networks consist of a collection of neurons. Each neuron is connected to a node that is further connected to other nodes. The components of neural networks are as follows:

Neural Networks

Neural networks are built of artificial neurons that function on the same biological concept of neurons. It works by receiving input and producing output using an output function. The initial inputs are external data like images and documents. The final outputs finish the task, such as recognizing an object in an image.

Connections and weights

The network comprises connections, and each connection further provides an output of one neuron as an input to another neuron. Also, each association has a designated weight that serves excellent importance. Each neuron can have multiple data and output connections to perform various functioning.

Propagation function

The propagation function measures an input to a neuron from an output. A bias term may be added to the result of the propagation depending on the algorithm.

Types of neural networks

Neural Networks are of various types that are used in networking. But the two most important which are frequently used are convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

Convolutional Neural Networks

Convolutional neural networks (CNNs) are often used for image recognition and classification. For example, suppose that you have a pair of photographs with a cat in it. CNN’s will check images thoroughly. The second set of outputs is used to know whether the image contains a cat.

There is no doubt that CNN’s need the hour as they perform various tasks related to data extraction. When CNN’s were not brought into the use, researchers would manually decide which characteristics of the image were most important for detecting a cat. Neural networks are capable enough to build up such features, determining which parts of the model are the most meaningful.

Recurrent Neural Networks

Recurrent neural networks (RNNs) are the foremost choice for building representations of data like handwriting recognition and voice recognition. This functions the same as CNNs as a cat can’t be detected looking at a single pixel. One can’t distinguish text or speech by looking at a single letter.

RNNs are capable of “remembering” the network’s past outputs and using such results as inputs into computations. Long-short term memory (LSTM) units or gated recurrent units (GRUs) can be added to an RNN, which can be a useful source to remember important details and ignore the irrelevant ones.

Applications

  1. Medical diagnosis
  2. Credit Rating
  3. Portfolio Management
  4. Voice Recognition
  5. Image Recognition
  6. Fraud Detection

 

Advantages of Neural Networks

  1. A neural network can perform tasks that a linear program is unable to perform.
  2. A neural network does not need to be reprogrammed again and again.
  3. It can be implemented in any application.
  4. It can perform any algorithm without any problem.

Disadvantages of Neural Networks

  1. The neural network requires proper training to function.
  2. Requires high processing time for large neural networks.