For most of us, concepts like deep learning and artificial intelligence are still foreign words. Most people who encounter these terms for the first time react with mixed feelings of doubt and intimidation. How to train machines and make them do human tasks? What justifies the human-like behavior of machines?

These are important and controversial questions in the field of deep learning. But man is still able to answer and resolve all doubts. Provided that you are willing to explore the applications of deep learning and artificial intelligence in your daily life. In this article, we explore ten interesting applications of deep learning and artificial intelligence in everyday life.

A brief look at the concept of deep learning

Machine learning and deep learning are both subsets of artificial intelligence. However deep learning is the evolved and advanced stage of machine learning. In machine learning, human programmers create algorithms that perform analysis using data.

Deep learning differs from machine learning because it works on an artificial neural network that is modeled after the human brain. Such machines with deep learning capacity do not need to follow the instructions of human programmers. Deep learning works through the huge amount of data that we create every day.

Deep learning models are superior to artificial intelligence in some ways. For example, in image recognition, deep learning algorithms are twice as effective as any other algorithm.

Suppose an artificial intelligence model reaches 50% accuracy. In this case, this device will not be suitable for use. For example, consider a car.

A person does not trust a car whose brakes work 50% of the time. Meanwhile, if the accuracy of the artificial intelligence model of the system reaches about 95%, it will be much more reliable for practical use. This level of accuracy can only be achieved with deep learning algorithms.

In the following, we discuss the applications that deep learning has had so far in various industries:

1. computer vision (computer vision)

Professional gamers frequently interact with deep learning modules. Deep neural networks have the power of image recognition, classification, and image restoration. Also, they are even able to recognize handwritten digits in a computer system. In line with training, deep learning rides on a super neural network to enable machines to mimic human vision.

2. Robots based on deep learning

Nvidia researchers have developed an artificial intelligence system that helps robots learn human movements. Today, household robots that perform actions based on AI inputs from multiple sources are commonplace. Like the human brain that analyzes events according to past experiences and emotions; Deep learning processes also help robots in performing tasks according to the ideas of artificial intelligence.

3. Automatic translation

Automatic translations also existed before the advent of deep learning. But deep learning helps machines provide translations with high accuracy; Accuracy that did not exist in the past. In addition, deep learning has also been effective in translation obtained from images; A completely new process that was not possible using traditional text-based interpretation.

4. Customer experience

Many businesses are already using machine learning in the field of customer experience. For example, online self-service platforms can be mentioned. Additionally, many organizations now depend on deep learning to create reliable workflows. Most of us are already familiar with chatbots that are used by organizations.

Deep learning can be used for speech recognition to improve the customer experience. Speech recognition technology has been around for a long time, but it won’t become a marketable product until deep learning models arrive.

The new generation of users wants to communicate with devices and devices. Take, for example, Apple’s Siri, which also enables voice commands and voice recognition. Communicating with Siri is similar to interacting with a human.

Systems based on deep learning make decisions and execute specific commands by imitating human thought patterns and through neural network algorithms.

The neural layers of deep learning systems are not designed and built by engineers; Rather, it is these different data and information that lead to the progress and improvement of the learning process of these algorithms.

Siri’s user interface looks simple. But, the artificial intelligence algorithms designed in it are very complex.

Home automation systems and devices work through voice commands. In this context, deep learning can significantly improve the customer experience. Due to the evolution of deep learning applications, we can expect to see more developments in this field.

5. Self-driving cars

If you were lucky enough to see a driverless car in motion; Know that several AI models are working on it simultaneously. Some models are good at recognizing traffic signs and some are good at recognizing pedestrians. Many believe that self-driving cars will be safer than driverless cars.

6. coloring

In the past, adding color to black-and-white film was one of the most time-consuming jobs in media production. But thanks to deep learning models and artificial intelligence, adding color to B/W photos and videos is now easier than ever.

7. Image analysis and subtitle generation

One of the greatest feats of deep learning is the ability to recognize images and generate intelligent captions for them. Generating captions using artificial intelligence is so accurate that many online publications are now using such techniques to save time and money.

8. Text generation

Machines now can generate new text from scratch. They can learn the writing, grammar, and style of a text and write effective news. Journalist robots that are based on deep learning models; They have been providing accurate match reports for at least more than three years. And this skill is not limited to report writing.

Text production based on artificial intelligence is equipped to such an extent that it can analyze the comment section as well. Until today, text production has been very efficient in various fields, from topics related to children to scientific articles.

9. Language recognition

In this context, deep learning devices can recognize different accents. For example, first, the machine can understand that the person is speaking in English. Then it differentiates based on dialect. After recognizing the dialect, language processing is done by another artificial intelligence that is fluent in that language. There is no human intervention in any of these steps.

These are just a few examples of deep learning applications that exist so far. Deep learning provides a way for companies to develop learning modules. As more complex and richer algorithms are developed, companies will be able to achieve incremental growth.

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