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About a month ago Carles asserted on Twitter that Bayesian Neural Networks make no sense. This generated lots of good discussion, including a thorough response from Andrew Gordon Wilson defending BNNs. However, we feel that most responses missed the point of our critique. This blog post is a more thorough justification of our original arguments.
In this piece, I’m going to focus exclusively on how you can build software with these models, using a design pattern that will be immediately familiar to any web developer.
While preterm birth is still the leading cause of death among young children, we noticed a large number (24!) of studies reporting near-perfect results on a public dataset when estimating the risk of preterm birth for a patient.
We know that machine learning is the rage these days. But the machine learning technique that shines the most brightly is deep learning. Deep learning is all about how a computer program can learn through observation and make decisions based on its experience. Deep learning methods are useful for computer vision, natural language processing, speech recognition and processing, and so much more.
Facebook AI has built the first AI system that can solve advanced mathematics equations using symbolic reasoning. By developing a new way to represent complex mathematical expressions as a kind of language and then treating solutions as a translation problem for sequence-to-sequence neural networks, we built a system that outperforms traditional computation systems at solving integration problems and both first- and second-order differential equations.