It has
never been easier to build AI or machine learning-based systems than it
is today. The ubiquity of cutting edge open-source tools such as TensorFlow, Torch, and Spark, coupled with the availability of massive amounts of computation power through AWS, Google Cloud, or other cloud providers, means that you can train cutting-edge models from your laptop over an afternoon coffee.
Though not at the forefront of the AI hype train, the unsung hero of the AI revolution is data — lots and lots of labeled and annotated data, curated with the elbow grease of great research groups and companies who recognize that the democratization of data is a necessary step towards accelerating AI.
However, most products involving machine learning or AI rely heavily on proprietary datasets that are often not released, as this provides implicit defensibility.
With that said, it can be hard to piece through what public datasets
are useful to look at, which are viable for a proof of concept, and
what datasets can be useful as a potential product or feature validation
step before you collect your own proprietary data.
It’s important to remember that good performance on data set doesn’t guarantee
a machine learning system will perform well in real product scenarios.
Most people in AI forget that the hardest part of building a new AI
solution or product is not the AI or algorithms — it’s the data collection and labeling. Standard datasets can be used as validation or a good starting point for building a more tailored solution.
This
week, a few machine learning experts and I were talking about all this.
To make your life easier, we’ve collected an (opinionated) list of some
open datasets that you can’t afford not to know about in the AI world.
Computer Vision
MNIST
CIFAR 10 & CIFAR 100
ImageNet
LSUN
PASCAL VOC
SVHN
MS COCO
Visual Genome
Labeled Faces in the Wild
Natural Language
Text Classification Datasets
WikiTex
Question Pairs
SQuAD
CMU Q/A Dataset
Maluuba Datasets
Billion Words
Common Crawl
bAbi
The Children’s Book Test
Stanford Sentiment Treebank
20 Newsgroups
Reuters
IMDB
UCI’s Spambase
Speech
Most
speech recognition datasets are proprietary — the data holds a lot of
value for the company that curates. Most datasets available in the field
are quite old.
2000 HUB5 English
LibriSpeech
VoxForge
TIMIT
CHIME
TED-LIUM
Recommendation and ranking systems
Netflix Challenge
MovieLens
Million Song Dataset
Last.fm
Networks and Graphs
Amazon Co-Purchasing and Amazon Reviews
Friendster Social Network Dataset
Geospatial data
OpenStreetMap
Landsat8
NEXRAD
链接：
https://medium.com/startup-grind/fueling-the-ai-gold-rush-7ae438505bc2#.4ogf5l3xu
原文链接：
http://weibo.com/1657470871/EvlMEm0EH?ref=home&rid=7_0_202_2669536424773680536&type=comment
