We’ll be walking you through creating a robotic subsystem with a voice-activated motor leveraging machine learning (ML) and an Arduino Nano 33 BLE Sense.
July 03, 2022 by Jake Hertz
This article aims to contextualize machine learning (ML) for hardware and embedded engineers, what it is, how it works, why it matters, and how TinyML fits in.
June 05, 2022 by Brandon Satrom
This article will give a foundational understanding of quantization in the context of machine learning, specifically tiny machine learning (tinyML).
March 06, 2022 by Jake Hertz
Learn about a subsection of machine learning (ML) called Tiny Machine Learning (TinyML), what it is, its applications, hardware and software requirements, and its benefits.
January 09, 2022 by Jake Hertz
In this article, we’ll take a deeper look at how PUFs are classified, the attributes that make a “good” PUF, and design tradeoffs.
July 18, 2021 by Jake Hertz
Learn the key parts of an autoencoder, how a variational autoencoder improves on it, and how to build and train a variational autoencoder using TensorFlow.
April 06, 2020 by Henry Ansah Fordjour
This article discusses a complication that can prevent your Perceptron from achieving adequate classification accuracy.
February 06, 2020 by Robert Keim
This article shows you how to add bias values to a multilayer Perceptron implemented in a high-level programming language such as Python.
February 05, 2020 by Robert Keim
In this article, we’ll perform some classification experiments and gather data on the relationship between hidden-layer dimensionality and network performance.
February 04, 2020 by Robert Keim
This article provides guidelines for configuring the hidden portion of a multilayer Perceptron.
January 31, 2020 by Robert Keim
In this article, we’ll use Excel-generated samples to train a multilayer Perceptron, and then we’ll see how the network performs with validation samples.
January 30, 2020 by Robert Keim
This article explains why validation is particularly important when we’re processing data using a neural network.
January 28, 2020 by Robert Keim
This article takes you step by step through a Python program that will allow us to train a neural network and perform advanced classification.
January 19, 2020 by Robert Keim
This article discusses the Perceptron configuration that we will use for our experiments with neural-network training and classification, and we’ll also look at the related topic of bias nodes.
January 09, 2020 by Robert Keim
This article presents the equations that we use when performing weight-update computations, and we’ll also discuss the concept of backpropagation.
December 27, 2019 by Robert Keim
We can greatly enhance the performance of a Perceptron by adding a layer of hidden nodes, but those hidden nodes also make training a bit more complicated.
December 26, 2019 by Robert Keim
In this article, we’ll see why we need a new activation function for a neural network that is trained via gradient descent.
December 25, 2019 by Robert Keim
This article explains why high-performance neural networks need an extra “hidden” layer of computational nodes.
December 24, 2019 by Robert Keim
This article discusses learning rate, which plays an important role in neural-network training.
December 19, 2019 by Robert Keim
In this article, we’ll explore Perceptron training from a more theoretical perspective, focusing on the “error bowl.”
December 05, 2019 by Robert Keim
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