Top Deep Learning Applications Used Across Industries

In the world of technology, deep learning is a term used to meet the needs of an application in line with the rapid advancement of existing technology. The existence of time deep learning makes work faster, more efficient, and more accurate.

For a more detailed description, examples of implementing deep learning can be found directly in translation machines, digital assistants, search engines, customer service, and chatbots.

Not only that but deep understanding can also be found in applications such as Netflix, YouTube, etc. So, you want to understand more clearly what deep learning is? Here We have compiled the information just for you!

What is Deep Learning?

Deep learning or deep structured learning/hierarchical learning is part of artificial intelligence and machine learning, which is the development of multiple-layer neural networks to provide precise tasks. Examples include object detection, speech recognition, language translation, etc.

Despite being a subfield of machine learning, deep learning has different techniques. Why is that? Because deep learning automatically represents data, such as images and videos, to text without introducing code rules or human domain knowledge.

Deep learning was first developed for information in 1950, but it was only in 1990 that deep understanding could be applied successfully. In its application, the learning algorithm used is similar to the learning algorithm in the 1990s.

The only difference is that the algorithm model used has changed to be simpler, and resources have supported the development of the model. Apart from the model, the growth of deep learning data is also increasing, making it easy to manage.

As an illustration, to further understand the meaning of deep learning, one example of implementing deep learning is the face unlock feature on smartphones.

As you know, the face unlock feature is very efficient; smartphone users only need to detect their face by pointing the phone in front of their face, and then the smartphone will open automatically.

Deep learning capabilities are not only useful for an application but also the main technology behind self-driving cars. Not only that, but deep learning technology is also the key to voice control performance in running everyday devices, such as smartphones, tablets, and TVs, to hands-free speakers.

Deep Learning Applications

Without realizing it, many examples of implementing deep learning within an application exist. What are some examples of implementing deep understanding that needs to be recognized but is in an application? Come on, see the information that has been compiled below:

1. Google search auto-suggest

Google search auto-suggest is one of the features in the Google Search Bar. The existence of this feature allows Internet users to get various kinds of word recommendations even though they have not finished typing.

2. Home on Facebook or Twitter

The next example is the homepage or home feature on social media, such as Facebook, Instagram, to Twitter. This homepage feature displays uploads from friends on Facebook and Twitter.

3. Product recommendations on the marketplace

The next example of implementing deep learning is the product recommendation feature on several marketplaces. For fans of online shopping, this feature is very useful for them to find the items they like.

Deep Learning Applications for Diverse Industries

Deep learning is widely used in various sectors, especially in industries directly related to IT management. Launching from the MathWorks page, this deep learning example is implemented in the health sector for defence purposes. Here are some simple measures that you need to know.

1. Automotive Field

Deep learning is used in the automotive field, for example, in-vehicle automation systems or smart cars. The system will detect certain objects, such as stop signs or traffic lights.

Deep learning can also see the presence of sidewalks and pedestrian lanes. That way, this can minimize the risk of accidents.

2. Aviation and Defense Field

In aviation and defence, deep learning is used to identify objects from satellites in certain areas, including identifying safe and unsafe zones for the military.

3. Health Sector

In the health and medical fields, deep learning can be used to detect cancer cells. This is like being developed by the team at UCLA in making high-dimensional microscopes to collect data and identify and analyze using deep learning applications to make it more accurate.

4. Industrial Sector

The use of deep learning is also applied in the industrial world of automation. The aim is to improve the safety of workers, especially in the industrial sector, which relies on heavy equipment. In addition, deep learning is also used to detect environments deemed unsafe to provide early warnings for further security measures.

5. Electronic Field

The electronics field also uses deep learning to translate certain programming commands, for example, on devices that are home assistance in nature. So, with voice commands, the machine will work as instructed in the program.

How Deep Learning Works

Not only what deep learning is; you also need to know how this technology works. The deepest understanding works using the neural network method.

This makes it also known as deep neural networks, which operate at many levels or layers. As an illustration, traditional neural networks only have 2-3 layers available. Meanwhile, for deep networks, there are more than 150 layers.

One way of working that is widely applied to this type of neural network is Convolutional Neural Networks (CNN). CNN will perform by extracting features directly from the image data.

Data scanning and image data analysis processes will be more accurate in classifying objects.

Next, CNN will study the data and detect different features in the image using ten to hundreds of hidden layers. Each of these hidden layers or layers can be learned to become information as its output.

CNN is also widely applied in the identification of medical images, satellite images, and others.

Benefits of Deep Learning

In its application, there are many benefits to be gained from deep learning technology. Starting from making work more efficient, helping with manual labour, and so on. Want to know other benefits of deep understanding? Here’s the information.

1. Can generate auto features

Deep learning can produce new features without human intervention in it. This means that deep understanding can perform complex tasks that require extensive feature engineering.

2. It can work well even if the data is not structured

One of the other attractions of deep learning is its ability to work automatically even though the data is unstructured. This unstructured data includes text, images, and sound.

3. Supports parallel and distributed algorithms

In deep learning, parallel and distributed algorithms are on a fairly large scale. As an illustration, if you want to train a model on one computer, it usually takes up to 10 days. But with the parallel algorithm, the model can be distributed to several systems in less than a day.

Leave a Comment