Have you ever heard about ImageNet Large Scale Visual Recognition Challenge? The project’s objective is to evaluate algorithms for detecting and classifying images at large scale. Every year different software products compete with each other in detecting objects of ImageNet database. What is interesting in this competition is that since 2012 the neural network based on deep learning technology has been winning the challenge annually.
What Is Aactually Deep Learning?
Deep learning is a sub-field of artificial intelligence and a term to describe the methods of building the algorithms that learn from their experience without specialized software. Humans have no need to explain a machine how to solve a problem. It finds a solution by itself. For example, if we want an algorithm to detect the faces, we should show it thousands of faces, specify where a specific face is located, and then the software will learn how to detect it on its own. Machines can learn with a superviser (a techer) and without a superviser. However, the better results are demonstrated when machines are taught by supervisers.
How Does Deep Learning Work?
Deep learning mimics abstract thinking of humans. It can generalize as well. For example, a neural network poorly detects handwritten words. To avoid misinterpretation, all words must be uploaded to the network. Deep learning, on the other hand, uses multi-layered neural networks so it can easily cope with that task.
There are three terms which people consider to be interchangable: artificial intelligence, machine learning, and deep learning. However, you shouldn’t confuse those terms. Artificial intelligence is anything that is able to help machines complete humans’ tasks. Machine learning is a field of artificial intelligence where machines not only solve the problems but also learn from their experience. Deep learning is a field of machine learning that studies deep neural networks.
Let’s take a closer look at how it works. Imagine photo neural networks where a boy and a girl are depicted. On the first layer, neurons react on simple visual images, for example, brightness swings. On the second layer, neurons react on more complex things like angles and circles. By the third layer, neurons can react on inscriptions and faces. Neuron network detects intself what visual elements are interesting to it to solve the problem. It ranks the elements according to their priority to further understand what is depicted in the photo.
What Projects Have Already Been Developed With Deep Learning Technology?
Most deep learning projects are applied for detecting photos or audios, and disease diagnostics as well. For example, Google uses the technology for translations from images. Another project calles DeepFace uses the technology to recognize the faces depicted in the photos. In 2016, Google launched a project WaveNet that can mimic human language. For realizing this project, the company has uploaded millions of audio records of requests to OK Google. The system managed to build the right sentences with the roght order, accents, and emphasis.
Another feature of deep learning that is worth your attention is the ability to semantically segment the images or videos. It means that it not only detects what object is depicted but also identifies the contours of the object. This technology is used in self-driving cars to identify if there are any restrictions alongside the road. This technology is also used in healtcare to detect diabetic retinopathy in the photos of patients.
Is Deep Learning a Disruptive Technology, Will It Change Our Lives?
On the one hand, such huge companies like Google and Facebook have already invested millions of dollars in deep learning. They consider that neural networks with deep learning are able to change the technological world.
To sum it up, deep learning is a set of algorithms, a combination of math and code that resemble neurons in the human brain. Deep learning can achieve human-level accuracy in tasks like image recognition, voice recognition, and predictive analyticss. It’s basically machine perception. People have given machines eyes and ears and an ability to predict. Companies are applying deep learning for machine traslation, machine transcription (speeach to text), facial recognition, voice recognition, predictive maintenance, and healthcare.
Deep learning performs three kinds of analysis: classification, clustering, and predictions. Classification could be putting names to faces or applying a spam filter to email. Clustering could be image search, uploading a product photo and finding similar products. Predictions could be using a stream of current data to anticipate the future based on an understanding of historical patterns. This can be applied to vital statistics in healthcare or stock price action in securitites, on market forecasts for national economies.
Deep learning solves a lot of hard problems better than we have ever solved them before. It also provides a basis for new products and business lines because it lowers the cost of thinking, unlocks value in data we are already collecting and makes it possible for small teams of analysts to process much more data.