- That’s the vision of Andrew Ng, a founder of the Google Brain deep learning project, and former head of AI at Baidu–a position he left in March–who is today announcing a set of five interconnected online courses on the subject.
- “Today, if you want to learn deep learning, there are lots of people searching online, reading [dozens of] research papers, reading blog posts, and watching YouTube videos,” Ng tells Fast Company.
- As Ng sees it, getting to an AI-powered economy is going to take the work of much more than any one, or even several companies.
- “I hope we can build an AI-powered future that provides everyone affordable healthcare, accessible education, inexpensive and convenient transportation, and a chance for meaningful work for every man and woman,” Ng says in his announcement, which is the first from his newly created company, deeplearning.ai.
- Ng is aware that many people are still confused by AI, often getting bogged down in the different subspecialties, and lingo that can easily be misused.
The founder of Google Brain and former head of Baidu’s AI efforts wants to train a giant new workforce to help make “AI the new electricity.”
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- Automatic music transcription, inferring a musical score from a recording, is a long-standing open problem in the music information retrieval community.
- Identify precise onset times of the notes in a recording.
- The MusicNet labels apply exclusively to Creative Commons and Public Domain recordings, and as such we can distribute and re-distribute the MusicNet labels together with their corresponding recordings.
- MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note every recording, the instrument that plays each note, and the note’s position in the metrical structure of the composition.
- The labels are acquired from musical scores aligned to recordings by dynamic time warping .
MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note every recording, the instrument that plays each note, and the note’s position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results.
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