Data Optimization for Fantasy Sports Analytics

Fantasy sports represent a rich and exciting world of modeling and analytic possibilities. With the advent of modern computer vision, statistics tracking, and the general embrace of the sporting community of a “data-centric” view to the game, there is a wealth of information available about each player, their performances, and various metadata.

Transformation: AI

Mosaic sees Digital Transformation differently; our view is that while the technology is a critical part of any Digital Transformation, it’s only a part of a greater whole that includes people, process, and culture change that all combine to enable effective use of the technology.

Similarity Learning for Image Geolocation

We decided to approach this problem as a similarity learning modeling effort. We used convolutional neural networks to train a model that takes an image or video as input and outputs a vector representation of the input, such that similar inputs will be close to each other in the vector space. The vector learning is driven by a triplet loss function.

Few Shot Learning for Computer Vision

Object detection in video has become a matter of routine, however, expanding these models to detect an object of your choosing requires many thousands, if not tens of thousands, of training examples. Few shot learners seek to make this process cheaper and easier by learning to detect new objects with only a small handful of examples (i.e. 1-30).

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