Deep learning (DL) has gradually become one of the most popular areas in artificial intelligence after the 1990s. Deep learning is a branch of machine learning and uses neural layers to build models. It combines low-level features and gradually forms abstract representation features to model the input data. Deep learning builds neural networks that simulates the human brain for analysis and learning. It mimics the mechanism of the human brain to interpret data, such as images, sounds, texts, and sensor readings.

In machine learning, the quality of feature selection strongly affects the quality of the trained model. Feature selections approaches differ depending on the type of machine learning problem, e.g., supervised learning or unsupervised learning. For supervised learning algorithms two most popular feature selection techniques are Wrapper-based and Model meta-transformer approach. For unsupervised learning algorithms, filter-based approaches are widely used. In the part-I of this article, we will look into the wrapper-based feature selection approaches used for supervised learning problems.

Qeexo’s AutoML enables Machine Learning and AI applications development for a range of sensors. A comprehensive list of sensors includes Accelerometer, Gyroscope, Magnetometer, Temperature, Pressure, Humidity, Microphone, Doppler Radar, Geophone, Colorimeter, Ambient light, and Proximity. In this article, we will discuss two very important configurable parameters that apply to many of these sensors, Output Data Rate (ODR) and Full-Scale Range (FSR).

Qeexo AutoML supports three one-class classification algorithms widely used for anomaly/outlier detection; Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine. These algorithms build models by learning from only one class of data.

Inference settings contain two important parameters; Instance length and Classification interval. In this blog, we will explain the Classification Interval and in conjunction with raw sensor signals, ODR, Instance length, latency, and performance of the model on the embedded target.

Qeexo AutoML enables machine learning application developers to customize inference settings based [...]

Introduction Sound Recognition is a technology based on traditional pattern recognition theories [...]

For machine learning models, Sensitivity parameter reflects on how sensitive the model is for classes under consideration. Sensitivity Analysis is generally performed before deployment of ML models in the real world application. The primary objective of the Sensitivity Analysis is to make ML model lean more towards certain class(es) than the other(s).

Qeexo AutoML enables machine learning application developers to do analysis of different performance met- rics for their use-cases and equip them to make decisions regarding ML models like tweaking some training parameters, adding more data etc. based on those real-time test data metrics. In this article, we will discuss in detail regarding live classification analysis module.

This blog post is a companion to my talk at tinyML Summit [...]

The dream of every developer is to be able to write code [...]

Introduction Qeexo AutoML provides feedback on trained models through tables and charts. [...]

Starting with Qeexo AutoML 1.15.0, existing STWin users will have the option [...]

A demo Qeexo has shown at various trade shows that always gets [...]

It is not recent that AI has begun to find a significant [...]

In this article: PrerequisitesData CollectionData SegmentationBuilding ModelModel Performance & Live Classification In [...]

This document is intended to help you learn more about fundamental machine [...]

In this article, we are going to introduce you some of the [...]

In this article, we are going to introduce you some of the latest and greatest new features and improvements released in Qeexo AutoML 1.20.0.