What is Neural Network?
Our human brain and its complicated information processing methods inspired the architecture for neural network computing and deep learning. Deep learning is a type of artificial intelligence that provides machines the ability to execute specific tasks that typically require a human’s intervention. Neural networks use machine learning to access vast sets of data and subsequently learn from this data by discovering patterns in its training datasets, then applying the patterns to new, incoming datasets.
Neural networks are important in decision-making when human assistance is limited. A neural network learns the varied and nuanced relationships between data inputs and outputs allowing these networks to complete such nonlinear and/or complex tasks as making inferences from given data without actual training, such as statements or questions with similar meanings.
Neural Network Meaning
In data science, a neural network is an artificial intelligence (AI) method that teaches a computer how to “think” (process information) in a manner like a human brain. It’s a kind of machine learning, or deep learning, using nodes (or “neurons”) connected to each other in a deeply layered arrangement resulting in a computer system that can adapt or learn from its errors, thus continuously improving for greater and greater accuracy. This type of network can summarize a document, recognize a face, or produce solutions for other types of complicated processes.
A human brain cell is called a neuron, and these neurons work together as a network with incredible complexity, sending electric signals to one another when working on solving a problem, being creative, etc. The artificial neurons of a computer’s neural network are tiny modules of software, also known as nodes, that together create an artificial neural network, which is a software program (sometimes an algorithm) that can calculate solutions to various types of problems.
Neural networks are categorized by the way in which data flows. For instance, think of the game show, Jeopardy. Contestants are given an answer for which they must come up with the question. In an artificial neural network, this would be considered backpropagation.
On a math test, students are given a problem they must solve. For an AI neural network, this is known as feedforward.
Another type of neural network (quite possibly the most similar to an actual human brain) is the convolutional neural network. In this type of neural network, each hidden layer performs an explicit function, or convolution. This type of network is highly useful in networks responsible for recognizing and classifying images.
How a Neural Network Works
Modeled after the human brain’s neural architecture, a simplistic neural network basically connects these “neurons” within an input and output layer, between which is a hidden layer (or layers). Here’s what this looks like at the most basic level:
- Information enters the neural network through an input layer. There is only one input layer. The nodes in this layer process, analyze, categorize, etc. this information, then passing it to the next layer, a hidden layer.
- The hidden layer(s) obtain this processed information either from the input layer or from a different yet nearby hidden layer. There can be several hidden layers within an AI neural network. Each of these layers further analyzes, granularizes, etc. the information, then passing it to the next sequential layer.
- The final result, or solution, of the information processed by all previous layers is presented by the output layer. Output layers can have just one node or many nodes. The number of nodes within the output layer depends on the type of problems the neural network was designed to solve. If the network was created to solve simple yes or no questions, there is just one node that presents the solution in binary code (1s and 0s) to signify the yes or no solution. If the network was designed to solve classifications, its output layers may have multiple output nodes in order to satisfy the equation.
Neural networks designed to mimic a human brain as closely as possible in order to solve highly technical calculations may have millions of linked neurons with multiple hidden layers between the input and output layers. These types of networks are known as deep neural architecture. Exceedingly deep neural network architectures — in theory — can come to any conclusion thanks to the ability to map any type of input to any type of output.
However, the more intricate and complex a deep neural network, the greater training the network requires. Where some neural networks may only require a handful of examples to generate reliable output data, deep neural networks may require millions of training examples.
Pros of a Neural Network
A neural network is useful for:
- Diagnosing medical issues using classification of images
- Filtering social media behaviors for marketing to targeted audiences
- Using historic financial data to make future financial predictions
- Identifying chemical compounds
Neural networks are most often found in applications such as computer vision, speech recognition, and natural language processing, or NLP.
Cons of Neural Networks
Neural networks are only as good as their trainers and the information they’ve been provided. While a neural network learns quickly and can calculate exponentially faster than a human brain, if information used in training is incorrect or contains biases, the resulting calculations will also contain errors and biases.
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