Deep Brain AI

If you're a data scientist or a data engineer using Spark, you might be interested in SynapseML - a library for doing machine learning at scale with massive datasets.

SynapseML has several interesting features:

1. Responsible Al module, which gives detailed explanations of how features in opaque-box models affect the model prediction.
2. Support for Cognitive Services features like text-to-speech, text analytics, and multivariate anomaly detection.
3. Improved MLOps features with support of MLFlow
4. Support for geospatial intelligence to analyze distributed data on maps.
5. Support for tree ensemble model like LightGBM.

Release notes: https://Inkd.in/dukhsPg8
Paper: https://lInkd.in/dB3_r4rh

A distance measure is an objective score that outlines the relative difference between two objects (e.g., data points) in a problem domain.

In instance-based learning, distance measures play an important role. Usually training examples are stored verbatim and a distance function is used to determine which data instance of the training set is closest to an unknown test instance. Once the nearest training instance is located, its class is predicted for the test instance.

Following are a few algorithms in which distance measures at their core:

-- K-Nearest Neighbors
-- Learning Vector Quantization (LVQ)
-- Self-Organizing Map (SOM)
-- K-Means Clustering
-- K-medoids
-- Kernel-based support vector machines (SVM)
-- Knowledge graph embedding algorithm like TransE, etc.

Very insightful information on dimensionality reduction from Kyle McKiou.