Artificial Neural Network Course And Certification

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What Is Artificial Neural Network?

Artificial Neural Network (ANN) in the system of hardware or software, patterned after the operations of neurons in the human brain. ANNs also known as simple neural networks are a group of deep learning technology, that also falls under the shade of artificial intelligence known as AI.

Artificial Neural Networks are one of the fundamental tools used in machine learning. As suggested by the 'neural' part of its name, they are systems that are designed to replicate the same way we humans learn. Neural Networks are made up of inputs and output layers, also in most cases it has a hidden layer that is made up of units that transform the inputs into something that the output layer can use. They are an awesome set of tools for finding patterns that are far too complex or much for a human programmer to download and use to teach the machine to recognize.

Human brains understand the concept of real-world situations in ways that a computer cannot understand. The neural network was first developed in the 1950s to solve this issue. An artificial neural network is an attempt to imitate the neurons that make up a human brain so that the computer can be able to learn and make decisions in a more humanly way.

ANN was created by programming our regular computers to behave as though they are brain cells joined together. An eCommerce site for example, learns from user searches to improve your shopping experience.

Features Of Artificial Neural Network: 

Below are some of the features of Artificial Neural Network:

  1. Artificial Neural Network is a neurally implemented mathematical model.

  2. It contains a huge number of processing elements that are interconnected, called neurons to perform all its operations.

  3. Information saved in the neurons are just the weighted link of neurons

  4. The input signals get to the processing elements through connections and connecting weights.

  5. It has the ability to learn, understand, remember and generalize from the given data by suitable assignment and adjustment of weights.

  6. The general characteristics of the neurons describes its computational power, and no single neuron carries specific information.

Benefits Of Artificial Neural Networks

Some of the benefits of Artificial Neural Networks includes:

1. Organic Learning: Neural networks are built to learn organically, that is to say, an artificial neural network is not completely limited by the inputs and results that are being fed to them initially from an expert system. Artificial neural network has the capability to generalize the input being given to them. this ability is very important for robotics and pattern recognition systems like our voice searching software.

2. Fault Tolerance: Artificial neural networks usually have the potential for high fault tolerance. When these networks are used across many machines and servers, they are able to navigate around missing data or servers that cant communicate.

3. Nonlinear Data Processing: Nonlinear systems have the ability to find an easier way to carry out solutions that are computationally expensive. these systems also link connections between data points, instead of waiting for the records in a data source to be explicitly linked. This nonlinear shortcut mechanism is fed into the artificial neural network, these make it valuable in the commercial big-data analysis.

4. Self Repair: Artificial Neural Networks can do more than just routing around parts of the network that is no longer working. If they are asked to find out a specific data that is no longer communicating, these artificial networks can help to recreate large amounts of data by inference and help in showing the data nodes that are no longer working. These characteristics are useful for networks that need to inform their users about the current state of the network and effectively results in a self agonizing and debugging network. Scientists are currently trying to understand capacities assumptions and the applicabilities of various approaches that can efficiently improve the performance of the artificial neural networking system. 

In The Full Course, you will learn everything you need to know about Artificial Neural Network with Certification of Completion to showcase your knowledge and competence. 

Artificial Neural Networks Course Outline: 

Artificial Neural Network - Basic Concepts

Artificial Neural Network - Building Blocks 

Artificial Neural Network - Learning & Adaptation

Artificial Neural Network - Supervised Learning

Artificial Neural Network - Unsupervised Learning

Artificial Neural Network - Learning Vector Quantization

Artificial Neural Network - Adaptive Resonance Theory

Artificial Neural Network - Kohonen Self-Organizing Feature Maps

Artificial Neural Network - Associate Memory Network

Artificial Neural Network - Hopfield Networks

Artificial Neural Network - Boltzmann Machine

Artificial Neural Network - Brain-State-in-a-Box Network

Artificial Neural Network - Optimization Using Hopfield Network

Artificial Neural Network - Other Optimization Techniques

Artificial Neural Network - Genetic Algorithm

Artificial Neural Network - Applications of Neural Networks

Artificial Neural Network - Exams and Certification

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