The past few years have shown society how important and essential technology is. One of the most groundbreaking and innovative developments in this field is machine learning. Coming from the grounds of artificial intelligence, machine learning mimics the human brain, creating multiple levels of layers that represent complex data structures. But what exactly is this thing that we call deep learning? Stick around and read the whole article to find out.
The branch of artificial intelligence, or deep learning, uses special algorithms to read data and develop abstractions that help mimic the thinking process. Today, this technology is widely used in many sectors, for example, to understand human speech or recognize certain objects automatically. Similar to humans, deep learning functions as a human brain. Let’s say that it recognizes a friend and their face even though you had met that friend years ago.
You can say that this technology could also spot a familiar voice in a marketplace full of people. That’s why deep learning is unique, as it sets grounds for learning, planning, and executing various tasks to simplify work for humans. As our brain has more than a hundred billion cells, aka neurons, they build networks that we use to carry out different tasks and learn complex matters. Scientists took this parallel to build deep learning. Fast forward to today, computers learn and use intelligence very similarly to humans.
Deep learning moves data past multiple layers, and there are hidden layers as well. Each layer is a standard algorithm, usually known for having a single activation function. Since deep learning consists of more than three layers, including input and output layers, we call it “deep” learning, helping many receive the best and the most accurate results.
Here are the key facts regarding the layers of deep learning:
In other words, the output of the other layer holds the input for the following layer.
The first layer is the input layer, and the last one is the output layer.
The remaining layers between the input layer and the output are the hidden layers.
The observed information is received from the interaction of the different layers.
The data can be labeled or unlabeled.
The basic layer works like a human brain in terms of structure because it was designed in parallel with the human brain basis. While our brain is significantly more complex, artificial intelligence and deep learning algorithms helped pass the stigma, creating a superior technology that automates many important processes today.
The Working Principle
If we need to define deep learning, it’s a group of algorithms that function through layers, extracting higher-level features from the raw input. Some common examples of deep learning include image processing, voice recognition, or face scanning technology in biometric authentication. For instance, deep learning processes an image by using the lower layers to identify edges. In this case, then the higher layers can establish the concepts that are relevant to humans, such as their facial features.
Another illustration of a deep learning model would be an example of how the algorithms can be trained to detect certain data and learn from it. Let’s take the convolutional neural network deep learning model as an example. It can be used to analyze millions of data points, in this case, images. We can program this model to spot large numbers of dogs, for example. The network learns from the network by reviewing multiple image pixels. Then, the algorithm categorizes the groups of pixels that have a dog’s features, this way detecting the dog in the picture.
Some Use Case Examples
While deep learning has the potential to expand even further, there are some key discoveries have been made, all thanks to this technology. For example, deep learning helped to invent novel drugs and detect complete medical diseases. Not only that, but AI and deep learning experts suggest that this technology will bring value in the future for climate control. As the human population increases, the threat to the environment brings new challenges, which are predicted to be minimized through a new form of deep learning.
As of today, data-driven AI-powered applications use machine learning algorithms to enhance the capabilities of robots or guarantee cybersecurity for many businesses. Alongside humans, technology helps improve processes and increase efficiency in different sectors:
Agriculture. Deep learning helps farmers to utilize their equipment better, detecting specific tools based on crops and weeds plant needs. For instance, special weeding machines can be programmed to spray herbicides on selected plants, leaving others completely untouched. Deep learning is also used for harvesting, performing irrigation, and many more farming operations.
Healthcare. As discussed earlier, deep learning technology is essential for analyzing large amounts of data, especially pictures. Today, deep learning algorithms are used in medical imaging to analyze data and classify pictures. A great example of deep learning utilization in healthcare in cancer detection. That’s why dermatologists categorize skin cancers through deep learning. The same principle is applied when searching for retina diseases. On top of that, deep learning can predict medical events and help track medical records.
Fraud prevention. A big part of today’s banking industry and the rise of the fintech sector for customers is convenience. Banks and other financial institutions must take security measures despite the customer-first, efficiency-oriented approach. That’s why deep learning and AI are used for biometric authentication. The automated software scans customers’ ID documents and their selfies to check if the user registering on a banking platform is legit. Not only that but deep learning algorithms are also utilized in special digital tools, such as Business verification services that scan business partners and provide detailed reports about any company automatically. This way, organizations prevent getting involved in shady money laundering schemes.
Understanding how human brains work is one thing, but creating something that replicates such a complex matter is a whole different level. Robots and AI-powered tools minimize the need for real-life assistance to the point that businesses save time and money, allowing employees to complete their daily tasks more efficiently. It’s no secret that breakthroughs in science and technology are found thanks to AI and machine learning abilities. Who knows what else this revolutionizing technology will bring?