What Is Deep Learning and Why Is It More Relevant Than Ever?
Machine learning? Deep learning? Artificial Intelligence? These terms have become synonymous with the modern era; terms that people love throwing around in conversation on social media, and in think pieces. Nevertheless, properly understanding these terms will help put into perspective how some of the world’s most cutting edge technology will impact your life.
We have already touched upon artificial intelligence and machine learning but today, you are going to explore the lesser known cousin of these technologies, deep learning. So, it begs the question, how much do you know about deep learning?
For the uninitiated, deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Deep learning is part of the broader family of machine learning methods based on learning data representations.
A Brief Introduction
You may have read that and have gotten a little confused. In short, deep learning and all facets of modern AI uses data to make human-like “intelligent” decisions. Deep learning teaches computers to basically learn by example or data.
To put this into perspective, deep learning is used for driverless cars, enabling vehicles to recognize other vehicles, stop signs, and even pedestrians, while deep learning also lays at the center of consumer products like voice assistant drove smart speakers, facial recognition technology, and even on some popular web pages.
Today you are going to dive deeper into the world of deep learning and examine how this subset of machine learning will change your life.
The World of Deep Learning
Again, data is key and lays at the heart of deep learning. You may learn a new skill through practice and experience. Deep learning models do the same. Going back to the self-driving car example, a computer model might examine thousands of stop signs before gaining the ability to identify a stop sign.
Deep learning computer models learn to perform classification tasks directly from images, text, or even sound. A deep learning model can “ learn” to be accurate, even surpassing its human creators.
These models are “trained” to use large sets of labeled data as well as neural network architectures, something that we will explore later in the article.
Deep learning lays at the forefront of AI helping shape the tools we use to achieve tremendous levels of accuracy. Advances in deep learning have pushed this tool to the point where deep learning outperforms humans in some tasks like classifying objects in images.
Requiring high-performance GPUs, deep learning models utilize large amounts of labeled data. That driverless Tesla car that you are sitting behind needed millions of images and thousands of hours of video before gaining the ability to drive you home.
Learning to Make the Right Decision
Some of the most common deep learning methods used today use what is called a neural network architecture. Now, a neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
The beauty of a neural network is its ability to generate the best outcome without the need for redesigning of the output criteria. They can recognize patterns through data, and proceed to make an accurate decision.
Again,deep learning techniques rely on complex and layer heavy neural networks to identify an image, sound, or texts. Traditional neural networks might only contain 2-3 hidden layers, while deep networks can have as many as 150.
After creating your algorithmic model, what you have is a deep learning model that mimics the biological structure of the brain. Deep Learning is basically Machine Learning on steroids. Each layer processes features, and generally, each layer extracts some piece of valuable information.
As described by MIT News, "Modeled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected."
"Most of today’s neural nets are organized into layers of nodes, and they’re “feed-forward,” meaning that data moves through them in only one direction."
“An individual node might be connected to several nodes in the layer beneath it, from which it receives data, and several nodes in the layer above it, to which it sends data.”
So this begs the question, how is deep learning being used today?
Deep Learning Across the Industry
Deep learning models have already infiltrated your world, equally ushering in a range of breakthroughs in major industries ranging from the world of consumer electronics stretching its power to the realms of aerospace and defense.
More commonly deep learning is used in automated hearing and speech translation applications found on apps and smart device. Deep learning applications help these systems recognize your voice and provide accurate responses.
While in the medical field researchers are using deep learning to detect cancer cells. Even industrial companies are using deep learning to better the lives of employees, identifying when workers are at risk of hurting themselves while operating heavy machinery.
Deep learning tools will continue to change the way people work, create and even design products. This is only the beginning.
We interviewed one of the leaders dabbling in autonomous solutions in the heavy vehicle industry. This is what the company does.