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Three Applications for Deep Learning in Artificial Intelligence


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Deep learning is a sector of machine learning used to train computers to learn by example – or receive, process, and filter complex information with all five senses to produce a final output (similar to a human brain). It achieves incredible accuracy rates, ensuring products can be implemented in the real world safely.

From the virtual assistants in our phones to disease diagnostics, we can see deep learning everywhere. Here, we will cover the three most popular and progressive applications of deep learning.

  • Computer Vision (CV)
  • Natural Language Processing (NLP)
  • Audio Signal Processing (ASP)
  • What’s next?


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Computer Vision

One exemplary application of deep learning in computer vision. Computer vision is a field under deep and machine learning that allows computers to gain a high-level understanding from digital images or videos. It is now the fastest-growing sub-field and is applied to a wide range of use cases.

Computer Vision applications are rapidly gaining in importance across industries, prominently in manufacturing, automotive, oil and gas, retail, logistics, smart city, and agriculture. Such intelligent AI vision systems combine Artificial Intelligence with the Internet of Things (AIoT) and are built to collect video data from distributed camera sensors to interpret images with machine learning.

Most commercial AI vision systems are highly specialized and developed to automate visual inspection, remote monitoring, quality control, surveillance and security, organizational health, and safety, as well as to increase operational efficiency. Deep learning models can autonomously analyze the video stream of basically any camera sensor.


AI vision PPE recognition for helmet and vest detection
Computer Vision application for automated PPE detection in construction and energy industry applications

A recent trend named Edge AI allows deploying and running Deep Learning on physical Edge Devices, computers, or edge servers. The implementation of such distributed AI systems allows privacy-preserving, high scalability, and cost efficiency achieved through on-device computer vision inference.

Instead of sending the videos to the cloud, all videos are analyzed in near real-time, and only valuable metadata is collected in the cloud. Read about the advantages of Edge AI for Computer Vision.


Natural Language Processing (NLP)

Natural Language Processing, otherwise known as NLP, is another popular segment of deep learning. NLP merges artificial intelligence with human language. Because of the nuances and intricacies of language, NLP is seen as one of the most complex and difficult deep learning algorithms types to create.

For example, one word can take on several meanings in some languages, and NLP needs to be designed to recognize the surrounding context of that word and associate it with the correct meaning.

NLP is implemented using three main tactics: Part of Speech (PoS), parse trees, and semantics. In short, PoS defines the functions of individual words. Meanwhile, parse trees are used to determine the syntax of sentences (differentiating between verbs, nouns, adjectives, etc.).

With semantics, the computer learns to read through and understand the context (previous sentences) to deduce the appropriate meaning for a word.

The following image shows a great example application of NLP used to analyze notes of medical records to detect and remove or obfuscate sensitive personal identifying information (PII) and protected health information (PHI):


ML model to analyze medical records and remove private information PHI or PII from raw medical notes.


NLP has been used to create YouTube’s auto-captioning system or Apple’s Siri. Still, it is much less common when compared to other fields, such as computer vision. However, NLP remains a valuable application of deep learning.


natural language processing (NLP)
Explore the comprehensive guide to NLP technology


Audio Signal Processing (ASP)

Audio Signal Processing (ASP) in artificial intelligence is the process of applying algorithms and techniques to extract meaningful information from audio signals. ASP also involves using AI-based methods such as deep learning, reinforcement learning, and machine learning to process audio signals.

With the advances in technology, audio signal processing is increasingly used to build voice search and voice-activated programs. ASP often works in combination with NLP systems. Audio recognition is a large aspect of ASP, as it uses many of the same programming techniques to create. We encounter Audio Signal Processing around in automatic speech recognition when we get a voice message that gets automatically transcribed into a script by our phones.

This technology can be applied in a variety of applications, such as sound engineering and music production. In addition, ASP techniques are becoming increasingly popular in consumer electronics, where they are used to improve the sound quality of speakers, headphones, and other devices. Furthermore, it is being applied in healthcare and medical fields to detect abnormalities in speech and language, as well as in biometric applications to recognize voice patterns.


What’s next?

ASP, NLP, and Computer Vision are extremely powerful and fast-growing applications of deep learning. Read more about computer vision news and technologies:


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