Recommender Systems
With Nearist and just 500 milliseconds, you can: Accelerate drug discovery by comparing 2 billion molecules
Searching Call Center Recordings
e-Discovery, Customer Support Tickets, Patent & Literature Search, Document Exploration
Visual Product Search, Image Copyright Protection, Satellite Imagery Analysis, Brand Detection in Uploaded Media
Video or Image Hosting Sites
Matching Ads to Users or Queries
Large-Scale Face, Fingerprint, or Iris Recognition
Clustering for Customer Understanding
Nearist has recently benchmarked exhaustive (or “brute force”) k-NN search on a dataset of 1 billion image descriptors (the deep1b dataset). A single server containing Nearist’s Vector Search Accelerator (VSX) cards was able to find Read more…
In part 2 of the word2vec tutorial (Part I below), I’ll cover a few additional modifications to the basic skip-gram model which are important for actually making it feasible to train. When you read the Read more…
This tutorial covers the skip gram neural network architecture for Word2Vec. My intention with this tutorial was to skip over the usual introductory and abstract insights about Word2Vec, and get into more of the details. Read more…
Researching machine learning algorithms and finding new applications of k-Nearest
Neighbor search.
Benchmarking Nearist hardware against alternatives including GPUs and approximate techniques. Helping customers apply Nearist hardware effectively to their problems.
Data analysis, statistical modeling, and data visualization professional with experience in project management and client-facing roles.
Machine Learning: feature engineering, deep learning, dimensionality reduction, statistical interpretation, data munging, dimensionality reduction, NLP, model validation/evaluation