Dive deep into the basics of Artificial Neural Networks

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Abstract digital and technology background.

The sector of Artificial Intelligence could be placed in the topmost position of technologies that could bring the humankind a lot of advancements in the future. Using the word ‘future’ will be almost inappropriate because the changes have already begun. The introduction of Artificial Neural Networks has made the machines take a great leap towards ‘being almost human’! As astonishing as it sounds, the Artificial Neural Networks indeed was, is and will continue its efforts to produce a great set of artificial decision makers. In this article we will Dive Deep into the basics of Artificial Neural Networks, letting you know more about this brainy tech!

Artificial Neural Networks

Artificial Neural Networks or Parallel Distributed Processing Systems follows the basic working strategy of the Biological Neural Networks. Just like those millions of neurons inside a human, there are millions of such ‘neurons’ comprising Artificial Networks too, helping in decision making. The basic aim of Artificial Neural Networks is to simulate the artificial neurons, make them recognize different patterns and to take human-like decisions.

An Artificial Neural Network consists of millions of neurons arranged in a layered format. Such neurons are defined as units and are connected together. The set of neurons in an Artificial Neural Network can be classified as Input Units, Hidden Units, and Output Units. Input units collect information from external sources, Hidden Units are a suppressed version of input units and Output Units collect information from the hidden units and delivers it. All the neurons in an Artificial Neural Network will be fully connected. Such connections are called as edges and each edge will be given some weights. According to the weight of an edge, the data transmission and processing of the node varies. If the weight is high, the data handled will be positive and if not, negative.

Artificial Neural Network is not an algorithm, but a framework which is useful for a good number of Machine Learning Algorithms. The ANN is used for complex data processing. An ANN itself doesn’t possess a specific program for it’s working. It learns from its environment and process data accordingly. For example, if you gave an ANN system, a picture of a dog as an input, it’ll identify the picture based on a couple of previous data given to it. It won’t recognize whether the given picture is of a dog or not by recognizing the picture as it is, but it compares the pictures of dogs it’s been provided with earlier and decides based on result matching. In short, you’ll have to provide sample inputs to the input units of an ANN once it’s set. The input units will process it to the hidden units and from there to the output units. Such transmissions are done after doing a specific set of processing in the data. After testing the ANN by providing it with such varied set of example inputs, finally, an ANN will be provided with a completely new set of inputs to perform. By analyzing the results frequent training will be done.

General format of Net input and output of an ANN

The general format of Net input provided to an ANN is:

y(in) = x1.w1+x2.w2+x3.w3…xm.wm

Net Output = F ( y (in) ) = Output (Function of Net Input).

Data Processing in Artificial Neural Networks

Data Processing in ANN is based on:

  • The network topology used
  • Adjustments of Learning
  • Activation Functions

The basic networks used in ANN are:

  • Single Layer FeedForward Network

Will have only a single layer of nodes.

  1. Multi-Layer Feed Forward Network

Will have an intermediate set of hidden nodes.

Feedback Network

It is one important classification of ANN. In a feedback network, the signals can traverse in both directions in a loop until an equilibrium is reached. Hence a Feedback Network is a dynamic network. A feedback network can be again split into two.

  • Fully Recurrent Network

In a Fully Recurrent Network, all nodes are connected together in such a way, that each node can get input and provide a processed output.

  1. Jordan Network

A Jordan Network consists of a closed loop structure of interconnected neurons or nodes, where an output provided by a node is fed as input for another. Literally, a node’s feedback is fed as input for another node.

Learning

Learning is a basic process underwent by any Artificial Neural Network. A learning process can be technically defined as a method of modifying weights or connection between neurons or units of an Artificial Neural Network. Learning can be of three ways:

  • Supervised Learning

As the name implies, Supervised Learning is done in the presence of a supervisor. Before providing an input to an ANN, we’ll have the actual precise output of that input. Once the input is fed to an ANN, the processed output is compared with the actual input. If they don’t match, the weights of neurons are matched until the desired output is produced.

  • Unsupervised Learning

Here, no supervisor is present for learning. A set of input vectors are provided for the ANN which will be converted as a cluster of input. After processing the output, the system itself should adjust it’s weighted to precise the results.

  • Reinforcement Learning

This type of learning process undergoes with the availability of feedbacks. According to feedbacks, an Artificial Neural Network should modify the weights between its neurons to provide better results.

Trends in Artificial Neural Networks

The vast possibilities opened by ANN are always rich and hence the technology is dominating a good number of sectors with its effect. Let’s see the trends in ANN.

  • Capsule Networks

-A substitute for Conventional Neural Networks (CNN)

-Capsule Networks can handle and maintain hierarchical relationship between neurons.

– Less data is needed for training and the training strategy is simple.

-High Accuracy in performance.

 

Convolutional Neural Networks (CNN)

  • widely used technology.
  • works basically upon the way the human brain reacts to human vision results.
  • used in visual recognition technology.
  • also useful in sectors like robotics, self-driving, image recognition, image classification etc
  • preferred by data scientists.
  1. Deep Reinforcement Learning (DRL)
    • Neural Networks learns from communicating with its environment and analyzing.
    • Applied mostly in Gaming sectors.
    • Useful for developing business applications.
    • Neurons undergo simulated training.
    • No labelled data used.
    • Data training cost is minimal.

Trends in ANN are not really a listable topic. Here are a couple of other Trends by ANN:

  • Learning
  • Long Short-Term Memory Networks
  • Hybrid Systems
  • Neuro-Fuzzy Systems
  • Neuro Genetic Systems

Now you’ve been briefed with a Deep Dive to the Basics on Artificial Neural Networks. This emerging or already emerged technology is giving astounding results with each result underwent. The era of self-driving cars and robots occupying our roads and machines making decisions for us isn’t any far. Data scientists are in no way going to leave the hypothetical possibilities once raised by this technology. They’re giving full assurance on making those possibilities a reality and we’re anyway witnessing the great leap of technological advancements with the usage of Artificial Neural Networks. In the future, we’ll be gifted with a heap of advanced tools and services by machines in various forms with the help of Artificial Neural Networks making our life easier and accurate. The way progress is attained in this sector proves it multiple times!

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