What Are Some Deep Learning Applications in 2020?


What is Deep Learning? This technology has been applied to several fields including speech recognition, social network filtering, audio recognition, etc. In this article, we’ll discuss some of the topmost and widespread applications of Deep Learning.

September 2, 2020 | AtoZ Markets – Alan Turing, in 1947 said that “what we want is a machine that can learn from experience.” His words can be marked true today as we have Deep Learning. It is a new machine learning technique that imitates the way we human beings gain knowledge and learn through examples.

What is Deep Learning?

Deep learning is one of the branches of machine learning in the field of Artificial Intelligence, commonly known as AI. Deep learning includes statistics and predictive modeling, and hence it is an essential element of data science.

Deep learning makes the process faster and easier, especially when it comes to tasks related to data science like collect, analyzing, interpreting, and everything that deals with working on a large amount of data. Let us discuss a few of the topmost and widespread applications of Deep Learning.

Fraud News Detection and News Aggregation

Every day, The internet is becoming the primary source of all genuine and fake information. Fraud news detection has become an essential asset in today’s world. It has become challenging to distinguish between the false and the real news as bots replicate it across channels automatically. The Cambridge Analytica is one of the best examples of how personal information, fake news, and statistics can influence reader perception.

Deep learning helps develop filters or classifiers that can detect fake news and remove it from the feed. It can also warn you of possible privacy breaches. It is challenging and complicated to train and validate a deep learning neural network for news detection since the data is cursed with opinions. The news is neutral or not and is not decided by one party. 

Earlier, we never had an option to filter out the ugly and bad news from the news feed. Extensive use of deep learning in the aggregation of news is strengthening efforts to customize reports as per readers’ choice. It might not sound like something new, but if we need to define the reader persona, further sophistication levels are being met to filter out news as per geographic, economic, social parameters, and the personal preference of the reader.

Natural Language Processing

It is also challenging for humans to understand the complexities of language, like semantics, tonal nuances, syntax, expressions, or even sarcasm. Humans learn to develop appropriate responses and a personalized form of expression to every scenario with continuous training since birth and exposure to different social settings. 

Using Deep Learning in Natural Language Processing is trying to achieve the same thing by training the machines to catch linguistic nuances and frame appropriate responses according to the situations. Answering questions, classifying text, twitter analysis, or sentiment analysis, language modeling, at an expansive level, are all subsets of NLP where deep learning gains momentum. 

Earlier logistic regression was used to build time-consuming, complex models. However, now distributed representations, convolutional neural networks, recurrent, and recursive neural networks, reinforcement learning, and memory augmenting strategies help achieve greater maturity in NLP. Distributed representations are comparatively useful in producing linear semantic relationships used to build phrases and sentences and capture local word semantics with word embedding.

Virtual Assistant

We must have used or at least heard about Siri, Alexa, and Google assistant. These are the most popular applications of deep learning in virtual assistants. Every interaction with the assistants is like a new opportunity for them to learn more about your voice and accent, hence providing you with a virtual human interaction experience. 

To know more about the subjects, virtual assistants use deep learning – for example, your song preferences of your most visited spots, or your favorite person to call. They learn to understand the commands by evaluating natural human language so that they can execute them. Another best capability of virtual assistants is to make notes for you, translate your speech to text, and book appointments. 

Virtual assistants are literally “I got your back” person for you as they can do everything from running your daily chores to auto-responding to your specific calls and coordinating tasks between you and your team members. With deep learning applications such as document summarization and text generation, virtual assistants can assist you in creating or sending appropriate email copies.

Healthcare

Deep learning is picking up the speed for the projects in the domain of Healthcare. NVIDIA says, “From medical imaging to analyzing genomes to discover new drugs, the entire healthcare industry is in a state of transformation, and GPU computing is at heart.

Applications and systems accelerated with GPU and delivered new efficiencies and possibilities, empowering physicians, clinicians, and researchers passionate about improving the lives of others to do their best work.” With the help of deep learning and neural networks, healthcare providers bring down the costs and mitigate health risks associated with readmissions.

AI is also exceedingly used in research by regulatory agencies to find cures for untreatable diseases in fields of the clinic. However, physician’s distrust and lack of an extensive dataset are still posing challenges to the use of deep learning in medicine.

Colourization of black and white images

Image colorization is the process of taking the input as a grayscale image and producing the output in the form of a colorized image, which represents the semantic colors and tones of the information. This process was previously done by hand with human effort, considering the difficulty of the task.

However, with Deep Learning Technology, it is now applied to objects to color the image, just as the human operator’s approach. This approach involves using high-quality convolutional neural networks in supervised layers, which will recreate the image with the addition of color.

Conclusion

It is a fact, along with being a hyperbole, that Deep Learning is achieving modern and advanced results across a range of challenging problem domains. There are many amazing and inspirational applications of deep learning that make life a little better and smarter than yesterday.

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