Neurons play a pivotal role in the proper functioning of our brain. Infact a neuron is the basic working unit of the brain. It is a specialized cell designed for transmitting informational signals to other nerve cells, muscle, or glands. And now with the advancement in science and technology, this neural model has found a place in functioning of machines as well. Known as the Artificial Neural Network (ANN), it incorporates with methodologies like machine learning and artificial intelligence to make machines smarter than ever.

What is Artificial Neural Network?

An artificial neural network is a series of algorithms composed of artificial neurons or nodes that is inspired by biological neural network of our brain. It imitates the way the human brain operates and learns to perform the tasks by taking pre-defined examples without having to re-program over and over again with any task-specific rules.

Application of Artificial Neural Network in Machine Learning

• Feedforward Neural Network – Also known as Perceptron, It can be applied in case of computer vision and speech recognition where classifying the target classes are complicated.
• Kohonen Self Organizing Neural Network –– it is used for recognizing patterns in the data. This type of neural network can be found in medical analysis to cluster data into various categories.
• Recurrent Neural Network (RNN) – In Machine Learning, RNN can be applied in text to speech (TTS) conversion models. In general, these kind of networks are a good option when it comes to advancing or completing information, like auto completion. Some recent advancements in this field have shown the usage of RNN in forecasting as well.
• Convolutional Neural Network- Popularly known as ConvNet, convolutional neural network
  is a class of deep neural networks, most commonly utilized for analyzing visual imagery. Techniques like signal processing and image classification uses ConvNet. It also dominates the domain of computer vision techniques, as it has high accuracy in image classification.

Apart from this, ANN has a very wide scope of application in areas like

• Language processing and translation
• Route detection
• Fraud detection
• Cancer detection
• Satellite imagery processing for agricultural and defense usage etc.

Key Benefits of Artificial Neural Network

• Image Processing and Character recognition – ANNs plays a big role in image and character recognition. Character recognition like handwriting has lot of applications in fraud detection (e.g. bank fraud) and even national security assessments. Image recognition has applications like facial recognition in social media, cancer detention in medicine and satellite imagery processing for agricultural and defense usage.
• The ANN research now has paved the way for deep neural networks that forms the basis of “deep learning” and which has now opened up all the exciting and transformational innovations in computer vision, speech recognition, natural language processing and famous examples like self-driving cars.
• Forecasting – Forecasting is required extensively in (e.g. sales, financial allocation between products, capacity utilization), in economic and monetary policy, in finance and stock market.
• Forecasting problems are complex, for example, predicting stock prices is a complex problem with a lot of underlying factors. Traditional forecasting models throw up limitations in terms of taking into account these complex, non-linear relationships. Whereas Artificial Neural Network can provide ability to model and extract unseen features and relationships.
• Loan applications – After going through a large number of historical cases, the questionnaire for the applicant’s loan eligibility is either accepted or rejected. Artificial Neural Networks have the ability to automate this process of approving loans.
• The most popular areas where Artificial Neural Network are being applied include learning, pattern recognition and interpretation of noise and incomplete inputs.

Examples: Google uses a 30-layered neural network to power Google Photos as well as to power its “watch next” recommendations for YouTube videos. Facebook uses artificial neural networks for its DeepFace algorithm, which can recognize specific faces with 97% accuracy. It’s also an ANN that powers Skype’s ability to do translations in real-time.

Artificial Neural Network is a coming of age technology, which can do wonders when combined with Machine learning. The true potential of this technology is still a subject matter of research, but one thing is clear, it is here redefine what we often call as ‘Smart Tech’.