Machine learning is used by many businesses to improve their workflows and enhance customer experience. As deep learning continues to mature, we are likely to see more applications taking advantage of this technology. In this article, we will discuss how this advanced form of artificial intelligence can provide benefits for practical applications, and we’ll present the top use cases of deep learning.
What Is Deep Learning?
Deep learning is a sub-field of machine learning. Both machine and deep learning are subsets of artificial intelligence, but deep learning represents the next evolution of machine learning. The machine learning process involves taking an input, such as an image, and identifying specific features like the shapes or classification of objects. Deep learning expands this capability to imitate the functioning of the human brain, which can continuously add to its understanding of features. Deep learning methods typically involve deep neural network architectures, from which the term “deep learning” is derived.
Deep learning machines don’t require a human programmer to tell them what to do with the data. This is made possible by the extraordinary amount of data we collect and feed to the deep-learning models.
Once a deep learning algorithm has gained enough experience, it can be used for specific tasks, such as detecting diseases, driving cars, locating weeds or pests in a field or finding faults in machines.
Recent advances in deep learning have reached the point where deep learning outperforms humans in some tasks, such as classifying objects in images.
The advantage of deep learning
To apply traditional machine learning to any problem, you first must perform a lot of pre-processing. In particular, you have to determine in advance which are the important properties or features in the problem domain. The advantage of deep learning is that it doesn’t rely on feature extraction. Instead of human experts explicitly specifying the features beforehand, deep learning finds the complex features by itself.
This robustness of deep learning has made it used for many applications. Deep learning for computer vision can be applied to any computer vision task involving classification. Deep learning also brought about great improvements in additional fields, such as speech recognition, and language understanding.
Deep Learning Use Cases
Here are several of the top use case of deep learning:
#1. Image recognition
Image retrieval and classification are very useful if your application utilizes images. Deep learning is using recognition models to sort images into different categories, or using auto-encoders to retrieve images based on visual similarity. Image recognition tactics can also be used to segment and classify video data.
Netflix, Amazon, Spotify, Facebook, and many more applications are dependent on their recommendation engines to provide a better service to their users. There are two recommendation categories—content-based, which generates recommendations to each user based on the personal activity history, and collaborative filtering methods that incorporate the behavior of the entire user base to make predictions for single users. Both of these strategies are using deep learning to produce useful recommendations.
#3. Text analysis and translation
Deep learning has been successfully applied to text analysis applications including document classification, sentiment analysis, and automatic translation.
#4. Chatbots—Online Customer Supports
Many companies use chatbots to service their customers, market their brand, and sell their products. By leveraging deep learning, chatbots understand the intent behind your customers’ requests, account for each customer conversation history when it interacts with them, and respond to their questions in a natural, human way.
#5. Speech recognition
Speech recognition includes several applications. The most widely used is “voice to text” which takes the spoken word and turns it into on-screen text. Deep learning has also been successfully applied to speech generation or synthesis, also known as ‘text to voice”. Speaker recognition is another area where deep learning has improved the ability to recognize the person who is speaking. This is especially important for security applications.
# 6. Autonomous driving
Deep learning has an important role in autonomous driving. Many deep-learning models are involved in driving an autonomous car. Two examples of such models are the one specialized in street signs and another aimed to recognize pedestrians.
One of the most crucial real-world problems today is cybersecurity. It’s easy to mutate malware and evade detection by even the most sophisticated traditional cybersecurity solutions. Deep learning can detect abnormalities in your network traffic and data changes which may indicate a cybersecurity threat. It can issue an alert and automatically take measures to remove the threat or isolate the infected file or system.
#8. Deep learning robots
A deep learning system can teach a robot to do a series of tasks by observing the actions of a human doing the same tasks. It imitates how a human brain learns from past experiences.
#9. Face recognition
Face recognition can be a challenge since faces might be similar to each other. Deep learning has proved to be accurate in face recognition, which makes it very useful in security applications.
#10. Voice Assistants
Deep learning is used by organizations such as Apple and Amazon to implement voice assistants. Apple uses deep learning to improve the speech of its voice assistant to make it talks with greater naturalness, personality, and expressivity. Deep learning can also help voice and virtual assistants like Siri and Alexa to better understand user queries.
#11. Medical Diagnosis
Deep learning is used to improve the diagnosis and treatment of diseases using medical data to recognize the patterns that categorize specific diseases. Deep learning can also be used to recognize patterns in x-rays, MRIs, and other diagnostic testing tools.
Deep learning is already deployed in many fields and applications. In this article, we’ve seen some of the top use cases of this technology. The growth of deep learning models is expected to accelerate and create even more innovative applications in the next few years.