👩‍💻Neural Network and its use-case🤖

“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.”

— Larry Page

What is Neural Network?

A neural network is a network or circuit of neurons, Neural networks are a set of algorithms, modeled loosely after the human brain, that is designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling, or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text, or time series, must be translated.

The most innovative feature of neural networks is that once trained, they learn on their own. In this way, they imitate human brains, which are made up of neurons. Neural networks and human brains do the transmission of information.

ANNs are composed of artificial neurons which are conceptually derived from biological neurons. Each artificial neuron has inputs and produces a single output which can be sent to multiple other neurons. The inputs can be the feature values of a sample of external data, such as images or documents, or they can be the outputs of other neurons.

How Does a Neural Network Work?

Machine learning algorithms that use neural networks generally do not need to be programmed with specific rules that define what to expect from the input. The neural net learning algorithm instead learns from processing many labeled examples (i.e. data with “answers”) that are supplied during training and using this answer key to learn what characteristics of the input are needed to construct the correct output. Once a sufficient number of examples have been processed, the neural network can begin to process new, unseen inputs and successfully return accurate results. The more examples and variety of inputs the program sees, the more accurate the results typically become because the program learns with experience.

Applications of Neural Networks :

Speech Recognition

Speech occupies a prominent role in human-human interaction. Therefore, it is natural for people to expect speech interfaces with computers. In the present era, for communication with machines, humans still need sophisticated languages which are difficult to learn and use. To ease this communication barrier, a simple solution could be, communication in a spoken language that is possible for the machine to understand.

Great progress has been made in this field, however, still, such kinds of systems are facing the problem of limited vocabulary or grammar along with the issue of retraining of the system for different speakers in different conditions. ANN is playing a major role in this area. Following ANNs have been used for speech recognition −

The most useful network for this is the Kohonen Self-Organizing feature map, which has its input as short segments of the speech waveform. It will map the same kind of phonemes as the output array, called the feature extraction technique. After extracting the features, with the help of some acoustic models as back-end processing, it will recognize the utterance.

Human Face Recognition

It is one of the biometric methods to identify the given face. It is a typical task because of the characterization of “non-face” images. However, if a neural network is well trained, then it can be divided into two classes namely images having faces and images that do not have faces.

First, all the input images must be preprocessed. Then, the dimensionality of that image must be reduced. And, at last, it must be classified using a neural network training algorithm. Following neural networks are used for training purposes with preprocessed image −

  • Fully-connected multilayer feed-forward neural network trained with the help of a back-propagation algorithm.
  • For dimensionality reduction, Principal Component Analysis PCA is used.

Conclusion

In this blog, I tried to explain to you about Neural Network, The neuron, Architecture of simple neural network, and deep learning neural network. Examples of neural networks.

Thank you for reading!!

Hope my blog is helpful to you😊

A computer Engineer Student

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