Deep Learning Tutorials ě°˝€” DeepLearning v0.1 documentation Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. For more about deep learning algorithms, see for example: The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU. The algorithm tutorials have some prerequisites. The code is available on the Deep Learning Tutorial repositories. The purely supervised learning algorithms are meant to be read in order: LSTM network
Bayes' Theorem Bayes' Theorem for the curious and bewildered; an excruciatingly gentle introduction. Your friends and colleagues are talking about something called "Bayes' Theorem" or "Bayes' Rule", or something called Bayesian reasoning. They sound really enthusiastic about it, too, so you google and find a webpage about Bayes' Theorem and... It's this equation. That's all. So you came here. Why does a mathematical concept generate this strange enthusiasm in its students? Soon you will know. While there are a few existing online explanations of Bayes' Theorem, my experience with trying to introduce people to Bayesian reasoning is that the existing online explanations are too abstract. Or so they claim. And let's begin. Here's a story problem about a situation that doctors often encounter: What do you think the answer is? Next, suppose I told you that most doctors get the same wrong answer on this problem - usually, only around 15% of doctors get it right. Do you want to think about your answer again?
Can Creativity be Automated? In 2004, New Zealander Ben Novak was just a guy with a couple of guitars and distant dreams of becoming a pop star. A year later one of Novak’s songs, “Turn Your Car Around,” had invaded Europe’s radio stations, becoming a top-10 hit. Novak had to beat long odds to get discovered. The process record labels use to find new talent—A&R, for “artists and repertoire”—is fickle and hard to explain; it rarely admits unknowns like him. So Novak got into the music business through a back door that had been opened not by a human, but by an algorithm tasked with finding hit songs. It’s widely accepted that creativity can’t be copied by machines. But now we’re learning that for some creative work, that simply isn’t true. The algorithm that kindled Novak’s music career belongs to Music X-Ray, whose founder, Mike McCready, has spent the last 10 years developing technology to detect musical hooks that are destined for the charts. Why, yes. Algorithms won’t only do work that requires a critical eye.