Deep Learning in the Digital Age: Top Applications of the Real-Time World

Ever since cognitive technologies have made an advent, it has been realized that machines are endowed with greater intelligence, autonomous thinking, and potential learning. Deep learning is a subset of the evergreen technology of Artificial Intelligence and is a part of broad machine learning methods.

What is Deep Learning?
Simply defined, Deep Learning is a concept that involves a set of algorithms that are inspired by human neural networks, and in the same sense, it can be branched with the concepts of machine learning.

Deep learning is related to the communication patterns of a biological brain; as well as with the way of structuring and processing the information. One of the main characteristics of deep learning is that you can learn at different levels of abstraction. That is, you can hierarchize the information into concepts.

Deep Learning Applications in the Real-Time World

#1 Image-Language Translations

The image-language translation was merely a thought, years back. But now we have an amazing application namely Google Translate. It automatically translates photographic images with texts into the preferred language and displays results in real-time.

Google Translate is known to promptly translate 100 different human languages, thanks to Machine Translation. The concept has allowed the world to break communication barriers and connect around the globe.

Deep learning has transformed the approach to machine translation and has derived some effective useful solutions to detect text/text images via machine learning deep learning techniques that have leveraged language translation.

Sequence-to-Sequence learning is used to solve distinct kinds of translation problems where the same algorithmic concept can be made to write AI chatbots and recognizing picture descriptions.

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#2 Colorization of Black & White Images

Image colorization is a concept of adding colors to black and white photographs and videos. It’s a new thing as surprisingly it was done with human hands which required a lot of effort and was not less than a hard nut to crack.

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The process is all about taking gray-scale images as input and producing colored images with semantic colors and tone as output.

To full the motive of the approach, Deep Learning algorithms are used and applied to objects and the context of the image using quality convolutional neural networks (CNN) in supervised layers that recreate the image/films with the addition of genuine colors.

Computer-enabled colorization started in 1970 and gradually took digital grounds and is now transformed into automatic image colorization. Many approaches have been used out of which Feed Forward CNN is prominently used, embedding the LAB model and its principles.

#3 Adding Sound to Silent Movies

Matching sounds to silent videos is an advantage served by the digital technologies where a great contribution is given by convolutional neural networks and LSTM recurrent neural networks.

The phenomenon that runs behind is training 1000 videos that produce different sounds on different surfaces that utilize deep learning to associate video frames with a database of pre-recorded sounds to select the appropriate ones for the scenes.

With deep calculations and computations, deep learning models tend to predict the best-suited sound(s) which are further validated as fake or real with a Turing-test setup.

#4 Fraud Detection

Frauds are unpredictable, dynamic, and without any pattern. With transactions occurring on digital platforms, frauds and sharp practices have dug their depth and have become more prevalent in the digital age.

Deep learning frameworks autoencoders and SOM(self-organizing maps) are used to limit financial frauds by detecting the identifying patterns in customer transactions and credit scores and by identifying anomalous behavior.

One can check out the details and the working phenomenon of Credit Card Fraud Detection using Deep Learning Models and analyze how the game revolves around sensitive datasets and numerical inputs and outputs.

While machine learning principles and paradigms are used to highlight fraud detection with classification and regression ML techniques, deep learning models are regularly being updated to minimize such deceitful practices via scaling efforts.

#5 Detect Repetitive Patterns and Segment Information

This one of the applications we are constantly exposed to. Today connectivity and the use of smartphone is almost the norm. We also use streaming platforms that serve us content based on our consumption patterns.

That happens thanks to deep learning. For example, Netflix, Amazon, or Spotify may recognize our interests to recommend music, products, or series based on that. The same happens with digital advertising on social networks.

Have you noticed that you always find advertising that is related to your tastes?
The reason is that deep learning learns from the information you share daily.

#6 Automatic Machine Translation

Convolutional neural networks can also be applied in recognizing images and objects and letters contained in them and later once identified, can be translated into appropriate words/phrases/texts.

This instant visual translation allows automatic translations into a specific language with a set of given words or texts. Further, the concept has been leveraged with deep learning which has received excepted results through Automatic Translation of Text and Automatic Translation of Images which are usually performed without preprocessing the sequences.

#7 Deep Dreaming

Imagination when collaborated with technology, led people to enhance features in an image using computers and led them to portray their imagination beyond limits.

Deep learning networks enabled a new concept in 2015, namely Deep Dreaming. This allows computer programs to hallucinate on top of an existing photograph and generate a resembled dream.

The outputs of the algorithm tend to vary as per the type of neural network and are further being experimented to open deep possibilities of using convolutional neural networks for induced deep dreaming experiences.

Deep Learning’s Role Play in Different Industries

More and more companies today are using machine learning for their applications, as they can make various predictions with them. Here is the list of the most frequent uses:

  • Predict customer preferences
  • Identify potential customers.
  • Target ads to customers.
  • Detect fraud and manage customer relationships.
  • Understand gene mutations, diseases, and therapies.
  • Analyze medical images in less time and with greater diagnostic precision.
  • Using images instead of using keywords to search for similar products or articles from a company.
  • Identification of company logos and brands in photos published on social networks.
  • Monitor reactions in online channels in real-time during the launch of a product.
  • Identify and follow the trust levels of customers, their opinions and attitudes in automated customer support services
  • Possible reuse of known and proven drugs for use against new diseases
  • Detect, prevent, and predict threats in real-time in the field of cybersecurity.
  • Locate faces and identify facial emotions.
  • Speech recognition.
  • Video classification.


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