Probabilistic Programming Is Efficient For Machine Learning: Gamalon

AI Software Juggles Probabilities To Learn From Less Data — Machine learning is becoming extremely powerful, but it requires extreme amounts of data. You can, for instance, train a deep-learning algorithm to recognize a cat with a cat-fancier’s level of expertise, but you’ll need to feed it tens or even hundreds of thousands of images of felines, capturing a huge amount of variation in size, shape, texture, lighting, and orientation. It would be lot more efficient if, a bit like a person, an algorithm could develop an idea about what makes a cat a cat from fewer examples. Read The Original Article