In the academic world, where are the latest machine learning technologies? There is an open source project on Github dedicated to updating the latest research breakthroughs, specifically which algorithms have achieved state-of-the art results on which data sets. Major categories include supervised learning, semi-supervised learning and unsupervised learning, transfer learning, reinforcement learning, and minor categories include phonology, computer vision, and NLP.
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This list includes almost all of the biggest breakthroughs in machine learning in 2017, From Microsoft’s 5.1% error rate for conversational speech recognition, to Hinton’s Capsule, which revolutionized deep learning, to Google’s “All in one model,” “Attention is All You Need,” and Facebook’s breakthroughs in machine translation, And the exciting AlphaGo Zero.
This is not only a list of papers and code resources, but also a list of 2017 machine learning and ARTIFICIAL intelligence milestones. Here, you can read about the breakthroughs made in machine learning field in 2017, and the breakthroughs made by various leading institutions and academic leaders.
“The library provides current best results for all machine learning problems and does its best to keep the library up to date,” the authors say, and we expect the list to continue to be updated, with more impressive new research findings that will keep ai moving forward.
Last updated on November 17, 2017
The classification of this library is as follows:
- Supervised learning
- Speech
- Computer vision
- NLP
- Semi-supervised Learning: Computer vision
- Unsupervised learning
- Speech
- Computer vision
- NLP
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