The Popular Applications of Deep Learning
Deep Learning (DL) is a subset of Machine Learning in Artificial Intelligence that imitates the functioning of the human brain in processing data and creating patterns for use in making decisions. It is an intelligent machine’s way of learning things, which enables it to learn without human supervision and grant them the ability to recognize speech, translate languages, detect objects and even make data-driven decisions.
In other words, Deep Learning is our best shot at creating machines with human-like intelligence.
Although Deep Learning is a branch of Machine Learning (ML), it is not restricted by a finite capacity to learn like traditional ML algorithms. Instead, DL systems can learn and improve their performance with access to larger volumes of data.
But, a big problem Deep learning has is wouldn’t have been possible without computers getting cheaper, faster, and smaller. The same is true for storage devices as large amounts of data need to be stored and processed for deep learning to become a reality. That’s the reason why although Deep Learning was theorized back in the 1980s, it became feasible only recently.
Deep learning is also a critical part of Data Science. It can help Data scientists to collect, analyze, and interpret large volumes of data and makes processes like predictive modeling faster and more efficient.
Here are some popular applications of Deep Learning that are shaping the world around us.
1. Self-driving cars
Relies to millions of data sets that simulate many real-world scenarios, Deep Learning models can help manufacturers ensure that driverless cars can handle unprecedented scenarios without causing harm to riders or pedestrians.
Machines can solve hypothetical scenarios, analyze and process the raw data collected from cameras, GPS, and numerous sensors then allow the automatic vehicles to identify and distinguish between lanes and road dividers, barricades, signs, pedestrians, slowing, or halted cars,…
2. Natural language processing
Natural language processing (NLP) is a field of artificial intelligence that provides machines the ability to understand, interpret, and derive meaning from human languages. Simply put, NLP helps machines to have the ability to converse with humans and even understand the contextual nuances of a language, such as Siri, Google Assistant, Google Translate…
3. Image recognition
Nowadays, the majority of accurate object detection systems you come across rely solely on deep learning. It does an impressive job of recognizing human faces, animals, places, and things with high accuracy. Google Photos is an excellent example of deep learning to classify and group images.
With the introduction of the Internet of Things (IoT), factories are getting smarter than ever before.
Thanks to the help of deep learning architectures like CNN, companies can replace the majority of human operators to spot defective products in the assembly line, which is more accurate and cost-effective, and chances of human error are eliminated.
Moreover, Deep learning algorithms can predict the chances of a manufacturing asset breaking down, helping reduce costs of losses as well as occupational accidents occurring.
5. Drug discovery
Deep learning can make the drug discovery & production process cheaper and faster. AtomNet is a deep complex neural network used for rational drug design. It’s a modern technology capable of finding novel and non-obvious drug compounds and can be a remarkable tool for accelerated drug repurposing projects. AtomNet was also used for predicting new candidate biomolecules for Ebola and multiple sclerosis (MS).
DL helps a lot for the financial industry because of its ability to process complex big data. By analyzing historical data, market parameters, and external factors that may affect a company’s performance, DL’s algorithms can quickly predict stock values with impressive accuracy, helping to create profitable trading strategies.
DL is also used in the loan approval process by analyzing historical data regarding approval and rejections, banks can rightly assess the risks of approving a loan to an entity.
7. Detect developmental delays
Early diagnosis and treatment of developmental disorders, autism, or speech disorders can positively impact a child’s future.
Using deep learning, the computer system that can identify speech disorders even before a child enters kindergarten, which a human can not do it.
8. Recommendation systems
Deep learning algorithms are used in recommendation systems to suggest content users are more likely to watch. These algorithms’ effectiveness is critical for platforms like Netflix, Facebook, e-commerce sites…
9. Fraud detection
Nowadays, fraud-related losses and damages are a sad reality of the financial industry. Financial scammers are growing.
Deep learning can help to detect a lot of fraudulent activities with the help of rule-based systems. For example, large transactions or the ones that happen in unusual places are indicators of fraud and can be easily detected.
Through some popular applications of Deep Learning, iRender hopes this will helpful for your work. If you want to learn more about Deep Learning, please click here.
At iRender, we provide a fast, powerful and efficient solution for Deep Learning users with configuration packages from 1 to 6 GPUs RTX 3090 on both Windows and Ubuntu operating systems. In addition, we also have 6 GPUs configuration packages of RTX 3080 and RTX 2080 Ti on windows OS (and will certainly be on Ubuntu very soon). You won’t have to worry about how much the RTX currently costs, how easy it is to buy it, or the problems with installing and maintaining it like we mentioned above. Moreover, with the 24/7 professional support service, the powerful, free and convenient data storage and transferring tool – GPUhub Sync, along with an affordable cost, make your training process more efficient.
Reference Source: learn.g2.com; www.mathworks.com; www.investopedia.com
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