Watch: Challenges and Opportunities in Applying Machine Learning
There are many opportunities in applying machine learning, whether as an individual developer or in a business. But how do you get started? This talk provides an overview that separates fact from fiction and proposes processes to find opportunities for applying ML. This includes understanding where ML can have... Read more
Deep Learning Research in 2019: Part 2
The deep learning revolution has continued to expand in 2019, affecting a wide range of fields from neuroscience to social media and more. In practical as well as theoretical applications, deep learning is growing more advanced and more influential. Below are some of the most interesting research papers published... Read more
Watch: Deploying Investments in AI and Machine Learning
Over the next 18 months, companies will be completing the R&D phase of their AI/ML investments and will be deploying their models and algorithms to production. The proper execution of deploying your AI/ML models will separate the organizations who see an ROI on AI from those who don’t. This... Read more
Designing Better Recommendation Systems with Machine Learning
Recommendation systems are among the most familiar applications of machine learning and artificial intelligence. Not only are these systems valuable to consumers who may be looking for anything from new shows to watch or a better options for airfare, but they are also important to the producers and advertisers... Read more
The Power of Universal Latent Space In Medical Breakthroughs
The cost to develop new medicines has grown tremendously despite our computing and medical advances. Although we have a greater need than ever for massive breakthroughs in medical science, the industry itself is still a slow, highly regimented field. Mason Victors, CTO and CPO for Recursion Pharmaceuticals believes that... Read more
The Complete Guide to Decision Trees (part 2)
(See part 1 here.) Now you may ask yourself: how do DTs know which features to select and how to split the data? To understand that, we need to get into some details. All DTs perform basically the same task: they examine all the attributes of the dataset to... Read more
Operationalization of Machine Learning Models
Lots of businesses want to use machine learning, but few are ready to integrate machine learning into a real-life context of operations. Dr. Mufajjul Ali, Data Solutions Architect for Microsoft, outlines how Microsoft is addressing these needs and offers some advice for businesses looking to operationalize ML models and... Read more
Watch: Project Feels – Deep Text Models for Sentiment Analysis
This video discusses the use of active learning, deep learning, Bayesian inference, and causality in Project Feels. This project, developed by the Data Science Group at the New York Times, sought to predict how likely a given article was to evoke a range of emotions. Thus project crowdsourced data... Read more
Watch: A Breakthrough for Natural Language
Natural language is valuable, but it is complex. With a 1,000 word vocabulary, a 15-word sentence can easily express more than 1e30 (a 1 with 30 zeros) different ideas. Today’s natural language processing is trained to bucket a sentence into one of a few thousand categories–which also means it... Read more
Ensemble Models Demystified
Ensemble models give us excellent performance and work in a wide variety of problems. They’re easier to train than other types of techniques, requiring less data with better results. In machine learning, ensemble models are the norm. Even if you aren’t using them, your competitors are. Kevin Lemagnen is... Read more