Deep Learning is an area that has at least scraped the surface of every field around us. The applications of this phenomenon know no bounds. Companies like Google, Atari, AlphaGo, ImageNet, Baidu and DeepMind are using Deep Learning to come up with revolutionary breakthroughs. We have compiled a list of the most common applications of Deep Learning to really show you its impact in our daily life.
Natural Language Processing
Natural Language Processing is the process by which the computer translates human language into a language that the computer can understand. Siri and Cortana are examples of NLP that most of you probably use every day. So how does Deep Learning fit into NLP? Here’s how. Consider this. You want to learn a new language. How do you go about it? You start by learning new words in the language, and understanding the usage. But, you will not really understand what works and what doesn’t, unless you are exposed to the language and learn from the usage, what is right and what doesn’t. This is exactly how Deep Learning is used in NLP. The computer “learns” by making use of something called word embeddings, which is implemented by Deep Learning. It is a technique where words and phrases are mapped to vectors of real numbers. This mapping is carried out by Neural Networks.
Do you use a smart phone? If yes, have you used the Google Photos Application? Were you amazed by the accuracy with which the Application classified all your photos into different categories? Did you wonder about the magic behind this accuracy? Well, the magic is Deep Learning.
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 the feature extraction step.
To perform these steps with accuracy, it is necessary to first have extensive training data that can be used while classifying the images. To perform classification, Deep Neural Networks are used. Why they’re used, is because they have the ability to learn on their own without explicitly being taught. Weights are assigned to every neuron in a layer. If the weights cross a certain threshold, then they trigger the next neuron. In this manner, the network observes and learns which patterns correspond to which specific activations.
Fraud Detection is one of the most crucial fields today. It is essential to find new methods to prevent fraud. Paypal, the online payment platform, is one venture that has religiously been using Deep Learning in Fraud Detection for the past few years with excellent results. Let’s focus on Credit Card Fraud Detection, since it is an area of interest for most of us. Certain patterns are formed about Credit Card usage by a certain customer based on his income, occupation, past spending habits, etc. If the usage of the card doesn’t fit the accepted range set by the patterns and it falls beyond the safety threshold, then this is treated as an anomaly.
Most Social Media giants today make use of Deep Learning. Big names like Facebook, Instagram, Pinterest, Google, even LinkedIn, are spending big money on Deep Learning. The major challenge faced by Social Media giants is mining patterns from the massive volumes of data sets they face. So, big steps are being taken to ensure that Deep Learning aids operations. Facebook has been taking huge leaps in the field of AI. Have you wondered in amazement how Facebook so accurately allows you to tag the right friend? Or give you friend suggestions? Or show you customized information on your Timeline? This is all done through Deep Learning. Google acquired DeepMind, an English Artificial Intelligence company. They are showing immense success not only in customizing user searches even more accurately, but even in the field of Robotics! Pinterest uses Deep Learning to make more accurate recommendations for Pins. LinkedIn acquired Bright, a job search startup 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. Social Media Giants are hiring Data Scientists and AI Engineers like never before. It truly is the next big thing!
Although a novel application of Deep Learning, some Recommendation Engines these days extensively use Deep Learning to provide the best recommendations to users. The most common example of Deep Learning in Recommendation Engines is Youtube videos. Have you ever wondered how Google recommends just the right videos? In fact, haven’t you often cursed the tech giant because you’ve spent hours on end on the platform, watching one video after another, since the most relevant videos are recommended to you? Deep Learning is the culprit behind this. Many features are taken into consideration when a recommendation is presented to the user. Collaborative filtering is used along with a ranking Convolutional Neural Network. The input features would be which video the user is watching, whether the user finished watching a particular video completely, whether a user “liked” or “subscribed” to a particular video or channel. Based on all this, user similarity is calculated using Collaborative Filtering by using parameters like similarity in age, IDs of videos being watched, searches for videos etc. Finally, the “highest scored” videos are recommended to the user.
Apart from Youtube, many other companies like Spotify, a digital music service use Deep Learning in their Recommendation Engines. Spotify uses Deep Learning to provide its users with the best possible music recommendations based on their preference using Collaborative Filtering and Deep Learning.
Thus, Deep Learning is a state-of-the-art model for many of complex problems. But, Deep Learning is still at an early stage. It is also quite resource-consuming. But, with growing performance caps in technology and exploding data, Deep Learning can be expected to have wider applications in the near future.
The era of Deep Learning has only begun. It is the next big thing in Artificial Intelligence. It is being applied in every field, some obvious and some not very obvious. The other applications are Medical Applications, Self driving cars, Customer Relationship Management, adding colour to black and white movies, optimizing games, automated hand writing generation, adding sound to silent movies etc. Sentiment Analysis is an area that is gaining a lot of traction, that uses Deep Learning. If you’re planning a career shift to Deep Learning, this would be the time. All the top tech companies are hiring. Hustle away!