Projects Tagged

Supervised Learning

Genetic Algorithms

AutoML Engine Using Neuroevolution

AutoML system that intakes arbitrary rectangular data and builds and compares thousands of deep neural networks via genetic algorithms (DeepNEAT) to build the optimal model for your data. This system is highly performant on both flat and time series data, having the capacity to encode many variations of RNNs and CNNs in addition to standard dense networks.

Recommender Systems

Social Event Time Recommendation Engine

This time recommendation engine takes an arbitrary number of calendar schedules as input and understands the context of the event based on features such as NLP embeddings of event name and description, as well as history of users past events. It output times that worked well for everyone and also made the most sense for the event in question.

Recommender Systems

Location Recommender for Social Events

Our location recommender utilized natural language embeddings of massive databases of location details to match locations to groups of users and their respective social events. This engine allowed for curated social events to be built automatically with everyone’s preferences in mind.

Computer Vision

Computer Vision Receipt Parser

Computer vision model that takes in an image of a receipt and outputs an itemization of all charges, each tagged with relevant item categories. To make this work properly, we needed to create a custom pipeline comprised of many deep neural networks. The first model performed object recognition, passed the output to an OCR model, then a model that we trained to understand receipt geometry, and a finally an NLP model that performed classification on the types of charges.

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