What is a Neural Network?

A neural network (NN) is a computing system loosely inspired by the structure of the human brain.  It provides a framework for multiple machine learning algorithms to work together to process complex data. A neural network can “learn” to perform tasks by analyzing examples, usually without task-specific instructions.

Why Neural Networks?

Neural networks, a component of machine learning, can be used to solve complex signal processing or pattern recognition problems. Commercial applications of neural networks include pattern recognition and forecasting. They’ve been used to recognize handwriting for check processing, transcribe speech to text, predict the weather, predict stock market fluctuations, recognize facial features, and plan and optimize delivery routes.  

Types of Neural Networks

Recurrent Neural Network

A widely used type of network is the recurrent neural network, designed to recognize the sequential characteristics in data and use patterns to predict the next likely scenario. Recurrent neural networks form a much deeper understanding of a sequence and its context and therefore make more precise predictions.

Due to their precise predictive results, recurrent neural networks are the preferred algorithm for tasks such as speech recognition, language translation, financial forecasting, and weather prediction.

Convolutional Neural Network

A convolutional neural network is used primarily for image recognition and processing, due to its ability to recognize patterns in images. While convolutional neural networks are designed to solve problems with visual imagery, they also have many applications outside of image recognition and analysis, including image classification, natural language processing, drug discovery, and health risk assessments.

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