Introduction to Application of Deep Learning
The following article provides an outline for Application of Deep Learning. Deep learning is a subfield of machine learning and is used in processing unstructured data like images, speeches, text, etc, just like a human mind using the artificial neural network. Deep Learning is rapidly changing the world around us by making extraordinary predictions in the fields and applications like driverless cars (to detect pedestrians, street lights, other cars, etc.), toxicity detections for different chemical structures, etc.
For example, looking at a picture and say whether it is a dog or cat or determining different objects in the picture, recognizing the sound of an instrument/artist and saying about it, text mining, and natural language processing are some of the applications of deep learning.
Various Applications of Deep Learning
Applications of deep learning are vast, but we would try to cover the most used application of deep learning techniques.
Here are some of the deep learning applications, which are now changing the world around us very rapidly.
1. Toxicity detection for different chemical structures
Here deep learning method is very efficient, where experts used to take decades of time to determine the toxicity of a specific structure, but with a deep learning model it is possible to determine toxicity in a very less amount of time (depends on complexity could be hours or days). Deep learning models are able to represent abstract concepts of the input in the multilevel distributed hierarchy. It enables multitask learning for all toxic effects just in one compact neural network, which makes it highly informative.
This model normalizes all the chemical structures of the compounds, ensemble them to predict the toxicity of possible new compounds from normalized structures. How deep learning is far better than other machine learning techniques? Please check out this paper [DeepTox: Toxicity Prediction using Deep Learning by Andreas Mayr1,2†, Gunter Klambauer1†, Thomas Unterthiner1,2†and Sepp Hochreiter1*].
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2. Mitosis detection/radiology
Determining cancer detection deep learning model has 6000 factors that could help in predicting the survival of a patient. For Breast cancer diagnosis deep learning model has been proven efficient and effective. CNN model of deep learning is now able to detect as well as classify mitosis inpatient. Deep neural networks help in the investigation of the cell life cycle [Source: Cell mitosis detection using deep neural networks Yao Zhou, Hua Mao, Zhang Yi].
3. Hallucination or sequence generation
Creating new footage by observing different video games, learning how they work, and replicate them using deep learning techniques like recurrent neural networks. Deep learning hallucinations can generate High-resolution images by using low-resolution images. This model is further used for restoring the historical data from low-resolution quality images by converting them into high-resolution images.
4. Image classification/machine vision
We see Facebook providing a suggestion for auto-tagging different persons in a picture is a perfect example of machine vision. It uses deep nets and takes pictures at different angles, and then labels the name to that picture. These deep learning models are now so advanced that we can recognize different objects in a picture and can predict what could be the occasion in that picture. For example, a picture taken in the restaurant has different features in it, like tables, chairs, different food items, knife, fork, glass, beer (brand of the beer), the mood of the people in the picture, etc. By looking at the images posted by a person can detect the likings of that person and recommend similar things to buy or places to visit etc.
5. Speech recognition
Speech is the most common method of communication in human society. As a human recognize speech understands it and responds accordingly, the same way deep learning model is enhancing the capabilities of computers so that they can understand how humans do react to different speeches. In day to day life, we have live examples like Siri of Apple, Alexa from Amazon, google home mini, etc. In the speech, there are lots of factors that needed to be considered like language/ accent / Age / Gender/ sound quality, etc. The goal is to recognize and respond to an unknown speaker by the input of his/her sound signals.
6. Text extraction and text recognition
Text extraction itself has a lot of applications in the real world. For example, automatic translation from one language to other, sentimental analysis of different reviews. This widely is known as natural language processing. When writing an email we see auto-suggestion to complete the sentence is also the application of deep learning.
7. Market prediction
Deep learning models can predict buy and sell calls for traders, depending on the dataset how the model has been trained, it is useful for both short term trading game as well as long term investment based on the available features.
8. Digital advertising
Deep learning models categorize users based on their previous purchase and browsing history and recommend relevant and personalized advertisements in real-time. We can experience the same, a product which you have just searched in your amazon application, advertisement of the same will be displayed in other applications like IRCTC.
9. Fraud detection
A deep learning model uses multiple data sources to flag a decision as a fraud in real-time. With deep learning models, it is also possible to find out which product and which markets are most susceptible to fraud and provide or extra care in such cases.
10. Earthquake prediction
Seismologist tries to predict the earthquake, but it is too complex to anticipate it. One wrong prediction costs a lot to people as well as govt. In an earthquake, there are two types of waves p-wave (travels fast but the damage is less), s-wave (travels slow but the damage is high). It is hard to make decisions days before, but by deep learning techniques we can predict the outcome of each wave from previous experience may be hours before but it is quick accordingly we can make adjustments.
Conclusion
Deep learning is making a lot of tough tasks easier for us. The applications of deep learning range in the different industrial sectors and it’s revolutionary in some areas like health care (drug discovery/ cancer detection etc), auto industries (autonomous driving system), advertisement sector (personalized ads are changing market trends). We have seen the major applications of deep learning, but still, there are lots of other applications some are worked upon and some will come in the future.
Recommended Articles
This has been a guide to the Application of Deep Learning. Here we discuss the introduction and various applications of deep learning respectively. You may also have a look at the following articles to learn more –
