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With the right features, almost any machine learning algorithm will find what you're looking for. Without good features, none will.

Reality AI Tools focuses on "inherent explainability" — keeping the fundamental functioning of each model conceptually accessible to the design engineer from the first steps of model construction.

Renesas uses a machine learning guided process to explore a huge variety of well-understood mathematical and engineering feature spaces.

As sensor and MCU costs decreased, an ever-increasing number of organizations have attempted to exploit this by adding sensor-driven embedded AI to their products.

The more sophisticated machine learning tools that are optimized for signal problems and embedded deployment can cut months, or even years, from an R&D cycle.

A project is something created by an individual/small team in a lab and works in a limited range of conditions; a product works everywhere and in all kinds of unpredictable conditions.

Reality AI Tools 4.0 allows customers to use artificial intelligence to reduce the cost of developing, procuring and manufacturing smart devices.

Bias in a technical, statistical sense can be a good thing – or at least a useful thing – so long as you recognize it, understand the effect it has and manage it.

This post offers tips on collecting data from high-sample-rate sensors for use with machine learning.

Machine learning projects can be successful through understanding ground truth, curating the data and not overtraining a machine learning model.