Neural Network definition and example explained in the simplest way.

Neural Network definition and example explained in the simplest way.

Neural Network definition

In simple words, if we try to explain the Neural network, it is a means of doing machine learning, in which a computer learns to perform some tasks by analyzing training examples. we can state this as systems of neurons, either organic or artificial in nature. It can adapt to changing input, so the network generates the best possible result without needing to redesign the output criteria.

an artificial neural network is composed of artificial neurons or nodes. Thus it is either a biological neural network, made up of real biological neurons or an artificial neural network, for solving artificial intelligence (AI) problems.

it is a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data.

Artificial intelligence subset

It is a functional unit of deep learning, and deep learning is a subset of machine learning and, machine learning is a subset of artificial intelligence.

To make it more simple, we will try to understand with the help of an example

Example

Nowadays we use it in our day to day life. From the security feature Face recognization or the google translate or on youtube the auto caption generator. it is a technology which makes machines possible to think like a human. The machine can think and process like humans. Basically it helps the machine to perform all the functions humans can do with their sensory organs. However, the result may not be accurate but it could be closer.

But in the future, as technology develops the results will be more accurate.

How does it work?

Tiered Arrangement of processors in AI Neural Networks.
Tiered Arrangement of processors in AI Neural Networks.

A neural network has a large number of processors. These processors operate parallelly but are arranged as tiers. The first tier receives the raw input similar to how the optic nerve receives the raw information in human beings.

Each successive tier then receives input from the tier before it and then passes on its output to the tier after it. The last tier processes the final output.

Tiered Arrangements.
Tiered Arrangements.

Small nodes make up each tier. These nodes are highly interconnected with the nodes in the tier before and after. Each node has its own sphere of knowledge, including rules that it was programmed with and rules it has learned by itself.  

The key to the efficacy of neural networks is they are extremely adaptive and learns very quickly. Each node weighs the importance of the input it receives from the nodes before it. The inputs that contribute the most towards the right output are given the highest weight/priority.

Types of Neural Networks

  1. Feedforward Neural Network.
  2. Radial Basis Function Neural Network
  3. Multilayer Perceptron
  4. Convolutional Neural Network
  5. Recurrent Neural Network
  6. Modular Neural network
  7. Sequence to sequence models

Feedforward Neural Network.

Feedforward neural network’s main attribute is forward in nature, No loops are generated once the signal passes forward then it can not come back.

A simple task which does not need any feedback can be carried out using feedforward neural network.

Radial Basis Function Neural Network.

RBF networks have three layers: input layer, hidden layer and output layer.

It is used for approximation function and recognizing pattern.

The radial basis function neural network is applied extensively in power restoration systems.

Multilayer Perceptron.

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. A multilayer perceptron uses a nonlinear activation

 

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. These layers can either be completely interconnected or pooled.

Before passing the result to the next layer, the convolutional layer uses a convolutional operation on the input. Due to this convolutional operation, the network can be much deeper but with much fewer parameters.

Due to this ability, convolutional neural networks show very effective results in image and video recognition, natural language processing, and recommended systems.

Recurrent Neural Network.

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

Modular Neural network.

A modular neural network has a number of different networks that function independently and perform sub-tasks. The different networks do not really interact with or signal each other during the computation process. They work independently towards achieving the output. As a result, a large and complex computational process can be done significantly faster by breaking it down into independent components. The computation speed increases because the networks are not interacting with or even connected to each other. 

Sequence to sequence models.

A sequence to sequence model consists of two recurrent neural networks. There‚Äôs an encoder that processes the input and a decoder that processes the output. The encoder and decoder can either use the same or different parameters. This model is particularly applicable in those cases where the length of the input data is not the same as the length of the output data.  

Sequence-to-sequence models are applied mainly in chatbots, machine translation, and question answering (ChatBots) systems.

Applications

  • Face recognition or face unlock.
    • In most of the phones nowadays the trending and most trusted feature of face unlock is possible just because of the Neural Networks. It provides proper security to the device.
  • Speech Recognition.
    • The smart IoT devices we currently use are able to hear our voice and what we are saying just because of the neural networks. Neural networks also perform a huge role in the development of IoT technology.
  • Translation Applications.
    • Applications like google translate which is able to translate the words or sentences via text, voice or pictures in several different languages are possible only because of neural networks
  • Auto-drive cars and parking assists systems.
    • the auto-drive and parking assist feature of the car is possible through the neural network technology.
PRO’SCON’S
Makes security features better and more secure via face recognition.For better performance and results powerful sensors are also required. Otherwise, the results will not be accurate and in face recognition, it can be unlocked via a photograph if sensors are not powerful enough to distinguish.
On the go we can give commands to smart devices via voice. Without interrupting our current work.Multi-layered yneural networks are usually hard to train, and require tuning lots of parameters
Once trained, the predictions are pretty fast.It is computationally very expensive and time-consuming to train with traditional CPUs.
large amount of academic research
used extensively in industry for many years
Neural networks depend a lot on training data. This leads to the problem of over-fitting and generalization. The mode relies more on the training data and may be tuned to the data
Pro’s and Cons.

Conclusion.

In our blog, we mention everything you need to understand about neural networks we try to keep it as simple as possible for your better understanding, this topic is easy yet hard to understand. it is one of the basic components of AI which makes it so important in the industry. developing artificial intelligence without Neural Network is almost impossible. Neural Network is still not capable of giving always accurate results and with the current Traditional CPU’s it works a bit slow.

We can’t fully depend on Neural networks until the result accuracy rate will be around 99%. If not how can we trust the self-drive cars and vehicles or many security feature Neural network has to offer.

Neural Network In Integration with the Artificial Intelligence can bring a big change.

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