We @fastforwardlabs recently released our sentiment analysis prototype Textflix. It uses movie reviews to show how natural language processing can unlock the data embedded in large amounts of unstructured text: https://t.co/MHBaE5ut88
#MachineLearning#NLP#dataviz
Design thread:
How can weak supervision help when you have limited labeled data? In our latest blogpost, we explore how to programmatically create labels with weak supervision. https://t.co/yUXhUjZBZe
#DeepLearning fun with tennis and computer vision. Automatically decode raw video to player-tracking data. This is works from a labeled dataset of only about 100 frames. Posting a few examples.
This just uses a single, non-customized Faster-RCNN (+keypoints) network. Originally there were separate stages for finding the court, segmenting point/non-point frames, & detecting players. But a single FRCNN network can do all this at once. A little surprised how well it works.
I wrote a thing about transfer learning, which has cat pictures and hand drawn robots... so, yeah, can't really think of a more convincing reason to read it than that. https://t.co/i2sgP6PkjW