This post addresses the appropriate way to split data into a training set, validation set, and test set, and how to use each of these sets to their maximum potential. It also discusses concepts specific to medical data with the motivation that the basic unit of medical data is the patient, not the example. If you are already familiar with the philosophy behind splitting a data set into training, validation, and test sets, feel free to skip this section. Otherwise, here's how and why we split data in machine learning. A data set for supervised learning is composed of examples.

As construction projects are large and include many stakeholders, digitalization is helping reduce confusion on-site and increase efficiency among workers. FREMONT, CA: In today's times, digitalization has become the vehicle of change. Technology has become the necessity of every industrial sector, and it holds the same for the construction business. The digital machinery that construction sectors are integrating into their workflow includes Machine Learning (ML), robotics, BIM, and 3D printing. Here are some of the digital transformations that the industry has observed.

Today we want to go a step further and implement product recommendation as well. Product recommendation are widely used and are implemented using so called Recommender Systems. There are different ways of implementing recommendations like those we can see on Amazon or Netflix for example. In our case, we will use a multi-class classifier that depending on the answer provided by the user, it will select the product with the highest probability. Using a classifier allows us to avoid having to store past customer behaviour to train the model.

I think it was Musk who said, 'Tinkering with AI is summoning the devil'. Sure, this is all very exciting. But when computers make decisions developers don't understand, we're undoubtedly, all in trouble.

You might not know it, but deep learning already plays a part in our everyday life. When you speak to your phone via Cortana, Siri or Google Now and it fetches information, or you type in the Google search box and it predicts what you are looking for before you finish, you are doing something that has only been made possible by deep learning. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. It also is known as deep structured learning or hierarchical learning. The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, and to Artificial Neural Networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons.

Retail Industry is looking at Artificial intelligence (AI) and machine learning (ML) as a solution to take their organization to the next level of productivity and customer experience. To explain this better, let us look at it this way – Retail companies have access to a massive amount of data about their customers and their shopping preferences. It is difficult for the companies to drill down into these huge mines of useful data and analyze it properly and derive actionable insights in real time. Therefore, massive amounts of this useful information could go waste which would otherwise have helped in increasing sales conversion rates or enhancing the customer satisfaction. With the help of AI and ML, the huge amount of big data could be used in creating web shops that take customer information and turn it into targeted shopping experiences or online chatbots that can easily answer questions and assist customers, or in-store intelligence to make the customer experience even more interactive.

Over the last few weeks, I have taken a deep dive into the world of Artificial Intelligence (AI). It's a space that requires continuous learning and the blogs that I have written are just a pin prick in the vast opportunities that this space presents. In thinking about intelligence and our future, my mind starts to work through the different models that surround us – from business models; to educational systems; and to seeing how natural systems are changing and adapting. Google defines intelligence as "the ability to acquire and apply knowledge and skills". A broad definition and I am sure it will be challenged on a few levels.

Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go – a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.

Heart failure is an important potential complication of type 2 diabetes that occurs frequently and can lead to death or disability. Earlier this month, late-breaking trial results revealed that a new class of medications known as SGLT2 inhibitors may be helpful for patients with heart failure. These therapies may also be used in patients with diabetes to prevent heart failure from occurring in the first place. However, a way of accurately identifying which diabetes patients are most at risk for heart failure remains elusive. A new study led by investigators from Brigham and Women's Hospital and UT Southwestern Medical Center unveils a new, machine-learning derived model that can predict, with a high degree of accuracy, future heart failure among patients with diabetes.

Another experience places the facial expressions and actions of visitors onto an animated character. Parts of the exhibit will translate a fairy tale written in Russian into English, show guests what a self-driving car sees out of its windows and have a computer guess if a person is feeling happy, sad, joyful or angry. Other displays feature AI to assist in playing a song on the piano, competing in ping-pong or even holding a conversation.