Machine Learning on Graphs: Knowledge Graphs and Embeddings
The nature of problems involving graphs calls for applying techniques tailor-made to work with them. In this post, we will focus on one class of graphs called Knowledge Graphs, describe some of the problems practitioners are trying to solve using them and explain how machine learning is used in this context.
Mapping Out Graph Analytics: Q&A With Daniel Salazar, Principal Data Scientist
Graph analytics is a classic network science technique that is making waves due to the advancements in Graph Database Technology (GDB) and the integration of machine learning techniques (i.e., neural networks), to solve a wide range of use cases. No need for a map to figure this one out – Principal Data Scientist, Daniel Salazar is giving us the full scoop as our guide.
Making Time for Scheduling Optimization: Q&A With Evan Lynch, Principal Data Scientist
We scheduled some time for this Q&A with Principal Data Scientist Evan Lynch, where we explored the many cross-industry challenges that scheduling optimization aims to solve, and the robust scheduling techniques that lie at the junction between mathematical optimization and machine learning.
Geospatial Data Analysis for Machine Learning-based Customer Clustering
Both descriptive analytics and machine learning models can benefit greatly from using geographic data analysis to solve segmentation use cases.
Q&A with Mosaic’s Resident Computer Vision Expert
In this Q&A, we sat with Principal Data Scientist Priya Karanth for her take on the latest innovations and most powerful applications behind the ever-changing landscape of computer vision. Let’s see what lies beyond the surface.
Making Sense of Natural Language Processing: Q&A With Alex Tennant, Senior Data Scientist
We sat down with Senior Data Scientist Alex Tennant for his perspective on the opportunities and complexities of NLP and how Mosaic is paving the way for the consistent evolution of this powerful technology.
Intro to Network Science and Graph Theory
Network science and simulation provide a robust framework for the study of network systems in all their complexity.
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.
A Guide to Addressing Culture in Digital Transformation
In this blog post, Mosaic examines how to identify & measure culture during a digital transformation.
Deriving value from IoT and sensor data with time series classification (TSC)
Anytime you wish to predict the transient state(s) of something or someone constantly monitored by sensors, time series classifications are the right tool. This article will explain some basic concepts of using deep learning models for TSC and finish with a brief discussion of ways to improve the performance to save on cost and speed.