What Deep Machine Learning Can Offer to Businesses

Moreover, these vectors represent the relationships between words as shown in the picture below.

These representations were later used to improve the accuracy of text classification, matching sentences, automatic categorization, question answering and other text-mining tasks.

Advanced image recognition

One of the most recent successful appliances of deep learning for image recognition came from Large Scale Visual Recognition Challenge, when Alex Krizhevsky applied convolutional neural networks to organize images from ImageNet, a dataset containing 1.2 million pictures, into 1,000 different classes. In 2012, Krizhevsky’s network, AlexNet, achieved a top-5 test error rate of 15.3%, outperforming traditional computer vision solutions with more than 10% accuracy.

The experience of Alex Krizhevsky changed the landscape of the data science and artificial intelligence field from the perspective of the research and business application. In 2012, AlexNet was the only deep learning model at ILSVRC (ImageNet Large Scale Visual Recognition Competition). Two years later, in 2014, there were no conventional computer vision solutions among the winners. And today, deep learning is, de-facto, a standard solution for computer vision tasks.

ELEKS’ experiment with deep learning for automatic colorization

Being early adopters by nature, we are continuously researching the capabilities of data-driven analytics and machine learning methods. One of our recent experiments featured deep learning neural networks applied for automatic colorization of old photos and videos. We have used residual autoencoder network suggested by Ryan Dahl and extended it to colorize a full-sized image.

The model for automatic colorization has been trained on the ImageNet dataset using TensorFlow library with GPU.

In the images below, you can see the “before” and “after” pictures and the videos we have coloured. The photos display the city of Lviv of the beginning of the 20th century and an episode from the 1939 movie Włóczęgi (The Vagabonds).

Original video:

Colorized video:

Deep Learning Meets the Business World

After its prominent success in the academic area, deep learning has been finally introduced into the business world. The industry giants, including Microsoft and Facebook, started to successfully use the advantages of this approach. The technology began to spread among verticals, bringing in advanced capabilities for speech and image recognition, stock trade, medical diagnostics and other.

Businesses are well aware that in the modern world data is the key to well-informed, sound decisions and reliable predictions. Companies have been continuously looking for better ways of leveraging the data they collect in order to make better decisions. Deep learning today not only offers them better accuracy of data processing - when compared to conventional models - but also solutions to the problems that were previously unsolvable. Here are some recent cases of how deep learning provided a significant business advantage.

Beating the churn

In 2015, MOZ revealed their deep learning model for customer churn prediction. The approach was based on the recurrent neural networks, a powerful model to work with sequential data. As customer behaviour is sequential in nature, this approach fits in perfectly.

The actions a customer is about to perform are caused by a vast variety of factors from the past. In relation to the customer, these factors could be external ( changing economic circumstances, rules and regulations, market demand), internal ( change in income, urgent needs, changes in tastes) as well as product related (previous experience with the product, the functionality required, the quality of the support). In combination, all this information provides much more insight compared to any single source.

The MOZ model uses an event aggregation approach to represent the customer lifecycle for a particular user along with the demographic and other factors in a single vector. This vector represents the user’s past behavior. Using this data, the recurrent neural network predicts the future behavior of this customer at every stage of the buyer's journey, based on the person’s activity in the past. The information that MOZ was interested in was associated with the possibility for the customer to shift from the free trial mode towards the purchase of the product or its withdrawal.

The model was tested on two types of churn: voluntary and involuntary. And in both cases, there was an obvious improvement in the prediction accuracy compared to conventional approaches.

Making better recommendations

Another approach was suggested by Microsoft Research in their paper A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems. The aim of the approach was to provide better recommendations to customers looking for Windows Apps, taking information from the search queries and browsing history as a base. A wide variety of factors was taken into account, including the news the person was searching for or the movies and TV shows that he or she was interested in.

The new, deep learning architecture that Microsoft has proposed was called “multi-view deep neural network”. This model analyzes user behavior in different domains (namely search queries, news read and movies/TV shows liked) and transforms this information into a single vision. This vision can be used later for making personalized suggestions about news, movies - literally anything.