Top 5 applications of Deep Learning
Deep Learning is an area under Machine Learning that of late, has been innately integrated with many aspects of our lives. In fact, it is surprising to realize that the simplest of online phenomena we face regularly is potentially an application of Deep Learning. Every sector, from e-Commerce to pharmacy, is using Deep Learning to come up with innovative products, services and solutions. The technology giants like Google, Amazon, Baidu, IBM, Apple, Microsoft and DeepMind (considered as the leader in Deep Learning research, now acquired by Google) are investing heavily in Deep Learning research to come up with revolutionary breakthroughs. Following is a list of the top 5 applications of Deep Learning, faced in our everyday lives.
Natural Language Processing (NLP)
How does Siri understand all the simple or difficult questions you ask it? Do computers really understand the human language? Well, no! That’s where Natural Language Processing (NLP) comes in. It is the process by which computers translate human languages into their own machine-readable logical forms. Developers use Deep Learning to “teach” machines the different ways of learning and using a new language through a tactic called “word embedding”. A neural network similar to that of human beings is built in a machine. Word embedding is done using this neural network to map words and phrases to vectors of real numbers.
Having crossed the primary barrier of language, computers now understand images as well. And this is the technology used by Facebook to pinpoint one person from your huge friend list, who appears in a photo you uploaded.
Image classification or object recognition is a technology where machines are fed with humongous amounts of data and then they learn to classify these images based on various object features. In Image Classification, there are broadly two steps.
- Feature Extraction — Extracting relevant features based on pixels.
- Classification — Classifying the image into the desired categories based on feature extraction.
Convolutional Neural Networks are used to implement this technology. These kinds of Neural Networks have the ability to learn on their own without explicitly being taught. Convolutional Neural Networks have layers of neurons and weights are assigned to every neuron in a layer. If the weight assigned to a particular image crosses the threshold, then the next neuron is triggered. In this manner, the network observes and learns to correlate patterns with objects or images.
This is one of the most common application of Deep Learning. Now, in fact, the “You may also like” and “Frequently bought together” features don’t even surprise us. It has become quite a habit. Not only do the e-commerce portals make use of recommendation engine, but other content sites such as YouTube, Spotify, Netflix & many other companies have their own recommendation engines. They do this through a combination of Collaborative filtering and a ranking Convolutional Neural Network. Initially, a user is tracked for the videos they watch which form the primary input used by the machines. After that, factors such as if an user finished watching a particular video or whether an user “liked” or “subscribed” to a particular video or channel is taken into consideration. Based on these, user similarity is calculated using Collaborative Filtering on parameters like similarity in age, IDs of videos being watched, searches for videos etc. Finally, the “highest scored” videos are recommended to users.
Most Social Media giants today apply Deep Learning. Big names like Facebook, Instagram, Pinterest, Google, even LinkedIn are spending big money on Deep Learning. The major challenge faced by the Social Media companies is mining usage patterns from the massive volumes of data that is being generated every moment. Steps are thus being taken to apply Deep Learning to automate procedures. It is astonishing how Facebook shows an Amazon ad on your timeline with the exact same boots you’d been checking on the website, despite there being no apparent connection between the two. Then there are the friend suggestions, the page suggestions etc. all done with the help of Deep Learning. Google acquired DeepMind, a British Artificial Intelligence company to customize user searches more accurately. LinkedIn acquired Bright, a job search start-up that uses Machine Learning algorithms for better matches. Bright uses Deep Learning to provide you with job suggestions in your exact field, location, preference etc. In order to innovate more in this field, the Social Media giants are hiring Data Scientists and AI Engineers like never before.
Deep Learning is being extensively applied in Fintech Fraud Detection nowadays. The different types of Fintech companies belonging to the Alternative Lending, Payment, Personal Finance and many other sectors like Kabbage, PayTM, Nerdwallet, Coinbase etc. are using Deep Learning for fraud detection in their systems. The types of fraud in this industry are also varied and evolving. However, technologies like Stacked Autoencoders (SAE) and Deep Belief Networks (DBN) are being used to design algorithms suitable for Machine Learning. To put it simply, let us take the example of credit card fraud detection. The machines identify a certain usage pattern for a credit card mapped to a particular individual, based on his income, occupation, past spending habits, etc. If the usage of the card doesn’t fit the accepted range and falls beyond the safety threshold, then the machines treat that as an anomaly thereby classifying the transaction as a fraudulent transaction. In light of the recent Fintech frauds, this application has been one of the most valuable applications of Deep Learning of late.
Few other applications of Deep Learning are self-driving cars, Customer Relationship Management, recoloring black and white images, optimizing games, automated hand writing generation, adding sound to silent movies and the list goes on.
Thus, Deep Learning is a state-of-the-art model that solves many complex problems. But, Deep Learning is still at an early stage. It is also quite resource-consuming. But, ease of access to good computational infrastructure and the growth in data, Deep Learning can be expected to have wider applications in the near future. If you’re planning a career shift to Deep Learning, this would be the time.