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Machine learning techniques have an ever-increasing importance in healthcare. Some key applications include medical image classification, treatment recommendations, disease detection, and prediction. This blog discusses predicting seizures in epileptic patients through binary classification.You don't need to be a neuroscience expert to develop a basic working prototype of a seizure prediction model. To classify electroencephalogram (EEG) signals we will use a long short-term memory (LSTM) recurrent neural network (RNN) with Deephaven.Deephaven is ideally suited for this task - its images for AI/ML in Python make using TensorFlow easy (for more information, please see our guide). Besides, in real-world applications, EEG data…

Understanding the physical world is a critical skill that most people deploy effortlessly. However, this still poses a challenge to artificial intelligence; if we’re to deploy safe and helpful systems in the real world, we want these models to share our intuitive sense of physics. But before we can build those models, there is another challenge: How will we measure the ability of these models to understand the physical world? That is, what does it mean to understand the physical world and how can we quantify it?Luckily for us, developmental psychologists have spent decades studying what infants know about the…

In my previous blog post, I created a simple, 3-step process for ingesting streaming data and performing face recognition live. I pointed my webcam at the Avengers: Endgame movie to show how my face recognition script identified each of the main characters and assessed the relationships among them. Now, as promised, I'll dive more deeply into how we can play with this data in the Deephaven IDE. I focus primarily on real-time plotting, creating a set of charts that update as my data streams in.As a refresher, here is the table and three graphs I produced in the first blog:This…