You may have come across terms like “deep learning,” “machine learning,” and “neural network,” but you might not fully understand what they mean.
In this post, you’ll learn more about them and why they should be important to you.
First, consider the brain. “The brain works like a big computer,” according to PubMed Health. “It processes information that it receives from the senses and body, and sends messages back to the body.”
These messages are sent through the billions of nerve cells that are arranged in patterns, which coordinate thought, emotion, behavior, movement and sensation. These nerves connect your brain to the rest of your body for near instantaneous communication. “Think about how fast you pull your hand back from a hot stove,” writes the Mayo Clinic.
How A Neural Network Is Like The Brain
“A neural network is a complex mathematical system that learns tasks by analyzing vast amounts of data,” writes Cade Metz at Wired, “from recognizing faces in photos to understanding spoken words.“
That almost sounds like how a brain works, right?
That’s because the concept, developed in the 1940s, was reportedly based on the human brain.
“Modeled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected,” writes Larry Hardesty at MIT.
Neural networks are initially fed large amounts of data. You tell it what the output should be based on what you’ve put into it. Over time, the neural network recognizes patterns and correlates them with particular labels.
For example, TechCrunch provides a detailed example of how neural networks can be trained to both identify and differentiate apples from oranges. “In its early stages, the neural network spits out a bunch of wrong answers in the form of percentages,” Ophir Tanz writes. “Since this is supervised learning with labeled training data, the network is able to figure out where and how that error occurred. … This process is repeated over and over until the neural network is identifying apples and oranges in images with increasing accuracy (and) when that happens, the neural network is ready for prime time and can start identifying apples in pictures professionally.”
Practical applications of neural networks, according to TechTarget, include “speech-to-text transcription, oil-exploration data analysis, weather prediction, and facial recognition.”
How TADA Is A Neural Network
TADA is a neural network in the sense that it is a living, breathing tool that learns your data inside and out. It handles data and delivers information with the same speed and power of the human brain.
If you are interested in learning more about how TADA can deliver near instantaneous insights to you, please contact us.