Read here to know how Machine Learning could help investors take data driven investment decisions and disrupt the stock market investment!
Google kicked off its annual I/O developer conference at Shoreline Amphitheatre in Mountain View, California on 8th May. Here are the 11 biggest announcements from Google I/O 2018.
Leveraging big data is imperative as information is at the core of business and growth of manufacturing businesses. In this blog article, we take a look at how big data analytics and machine learning are transforming the manufacturing sector.
Machine Learning is revolutionising the way we do things. But, before you start getting familiar with it, ascertain the difference between two most frequently used keywords in programming landscape: Framework and Library!
People use the terms Deep Learning and Machine Learning interchangeably, even though they’re not the same thing. So, what’s the difference?
New technologies often trigger unrealistic expectations in the market. It happened with the introduction of computers and yet again during the early years of the Internet. While adopting a new…
Cyber Security has become a major concern for every enterprise. The role of Artificial Intelligence in Cyber Security is to provide power and speed to tackle huge volumes of attacks with countless variations.
Banks have always had a close relationship with Data. From back in the days of the crusades when banks first came into existence by the Knight Templars , right to…
Nowadays, Retail Banks are more focused on finding or discriminating the right clients and the wrong ones (Defaulters). From a Credit Risk perspective, a Good Client will be a customer/applicant who has least chances to do default (a low-risk client) i.e. the applicant has low chances to perform default in his obligations. This detection process of identifying or separating a Good & bad applicant/client is where Credit Risk Scorecard comes into play.
Reinforcement Learning is a type of learning algorithm in which the machine takes decisions on what actions to take, given a certain situation/environment, so as to maximize a reward. The difference between supervised and reinforcement learning is the reward signal that simply tells whether the action (input) taken by the agent is good or bad. It doesn’t tell us anything about what is the best action.