Neural Networks: All You Must Know About It? 

There are myriads of types of Neural Networks and some new ones are getting developed as well. So, let us get to know about some of them and how they are used.

Perceptron

One of the earliest and most basic models of a neuron is the perceptron model, developed by Minsky and Papert, also known as TLU (threshold logic unit). It’s the tiniest neural network component and it performs specific computations to find features or business information in the incoming data. It receives weighted inputs and uses an activation function to produce the desired output.

Multilayer Perceptron

It is like a point of entry into more intricate neural networks where input data passes via several artificial neuronal layers. It is a fully linked neural network since each node is linked to every neuron in the layer below. There are input and output layers with many hidden layers, for a total of a minimum of three layers.

Its Uses:

1. Speech Recognition
2. Complex Classification
3. Machine Translation

Radial Basis Functional

An input vector, an output layer, and a layer of RBF neurons, with one node per class make up a Radial Basis Function Network. By comparing the input’s data sets to those from the training set, where every other neuron retains a prototype, classification is carried out.

Its Uses: 

The Radial Basis Functional Neural Network can be used in power restoration.

Feed Forward Neural Network

Again, this is the most basic type of neural network, where input data only flows in one way, passing via synthetic neural nodes and out via output nodes. Input and output layers are prevalent but hidden layers might or might not be. And, based on this, they can then be divided into single-layered or multi-layered feed-forward neural networks.

Its Uses:

1. Simple classification
2. Computer vision
3. Speech Recognition
4. Face recognition

Recurrent Neural Network

Recurrent neural networks are built to preserve layer output and feed it back to the input to aid with layer prediction. Moreover, the first layer is often a feed-forward neural network, succeeded by a recurrent neural network layer where a memory function remembers part of the information that was present in the preceding time step.

Its Uses: 

1. Text processing including auto-corrects and grammar checks
2. Image tagger
3. Translation
4. Sentiment Analysis
5. Text-to-speech processing

Sequence to Sequence Models

We can say that two Recurrent Neural Networks make up a sequence-to-sequence model. In this, an encoder handles the input in this case, and a decoder handles the output. And, working at the same time, the encoder and decoder can use the same parameter or a distinct one.

Its Uses:

1. Chatbots
2. Question answering systems.
3. Machine translations

Convolutional Neural Network

Rather than the usual two-dimensional grid, a convolution neural network has a three-dimensional layout of neurons. The convolutional layer refers to the top layer in which only a small portion of the visual field is processed by each neuron. You should know that like a filter, input features get gathered in batches. The network may perform these operations repeatedly to finish the full image processing even when it only fully comprehends the images in pieces.

Its Uses:

1. Image processing
2. Speech Recognition
3. Computer Vision
4. Machine translation

LSTM – Long Short-Term Memory

We can say that LSTM networks are a form of RNN which employs some special units along with conventional units. A “memory cell” found in LSTM units is capable of storing data in memory for extended periods. Again, you need to keep in mind that data is received in the memory, released, and forgotten under the control of a set of gates.

Modular Neural Network

A modular network consists of a number of distinct networks that each carry out a specific task. Throughout the calculation process, the various networks do not actually communicate with or signal one another but rather contribute separately to the outcome.

Its Uses: 

1. Prediction systems for the stock market 
2. Compression of high-level input data
3. Adaptive MNN for character recognition