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TinyML In Action—Creating a Voice Controlled Robotic Subsystem

TinyML In Action—Creating a Voice Controlled Robotic Subsystem

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.


Projects Jul 03, 2022 by Jake Hertz
Neural Network Quantization: What Is It and How Does It Relate to TinyML?

Neural Network Quantization: What Is It and How Does It Relate to TinyML?

This article will give a foundational understanding of quantization in the context of machine learning, specifically tiny machine learning (tinyML).


What Is TinyML?

What Is TinyML?

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.


How to Build a Variational Autoencoder with TensorFlow

How to Build a Variational Autoencoder with TensorFlow

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.


Understanding Local Minima in Neural-Network Training

Understanding Local Minima in Neural-Network Training

This article discusses a complication that can prevent your Perceptron from achieving adequate classification accuracy.


Incorporating Bias Nodes Into Your Neural Network

Incorporating Bias Nodes Into Your Neural Network

This article shows you how to add bias values to a multilayer Perceptron implemented in a high-level programming language such as Python.


How to Increase the Accuracy of a Hidden Layer Neural Network

How to Increase the Accuracy of a Hidden Layer Neural Network

In this article, we’ll perform some classification experiments and gather data on the relationship between hidden-layer dimensionality and network performance.


How Many Hidden Layers and Hidden Nodes Does a Neural Network Need?

How Many Hidden Layers and Hidden Nodes Does a Neural Network Need?

This article provides guidelines for configuring the hidden portion of a multilayer Perceptron.


Training Datasets for Neural Networks: How to Train and Validate a Python Neural Network

Training Datasets for Neural Networks: How to Train and Validate a Python Neural Network

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.


Signal Processing Using Neural Networks: Validation in Neural Network Design

Signal Processing Using Neural Networks: Validation in Neural Network Design

This article explains why validation is particularly important when we’re processing data using a neural network.


How to Create a Multilayer Perceptron Neural Network in Python

How to Create a Multilayer Perceptron Neural Network in Python

This article takes you step by step through a Python program that will allow us to train a neural network and perform advanced classification.


Neural Network Architecture for a Python Implementation

Neural Network Architecture for a Python Implementation

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.


Understanding Training Formulas and Backpropagation for Multilayer Perceptrons

Understanding Training Formulas and Backpropagation for Multilayer Perceptrons

This article presents the equations that we use when performing weight-update computations, and we’ll also discuss the concept of backpropagation.


How to Train a Multilayer Perceptron Neural Network

How to Train a Multilayer Perceptron Neural Network

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.


The Sigmoid Activation Function: Activation in Multilayer Perceptron Neural Networks

The Sigmoid Activation Function: Activation in Multilayer Perceptron Neural Networks

In this article, we’ll see why we need a new activation function for a neural network that is trained via gradient descent.


Advanced Machine Learning with the Multilayer Perceptron

Advanced Machine Learning with the Multilayer Perceptron

This article explains why high-performance neural networks need an extra “hidden” layer of computational nodes.


Understanding Learning Rate in Neural Networks

Understanding Learning Rate in Neural Networks

This article discusses learning rate, which plays an important role in neural-network training.


How to Calibrate a Microcontroller Internal Oscillator: A DIY Trimming Procedure Algorithm

How to Calibrate a Microcontroller Internal Oscillator: A DIY Trimming Procedure Algorithm

This article presents an algorithm intended for humans to calibrate the internal oscillator of an MCU, with the help of an oscilloscope and a spreadsheet. An example experiment with numbers is also shown.


Predicting Battery Degradation with a Trinket M0 and Python Software Algorithms

Predicting Battery Degradation with a Trinket M0 and Python Software Algorithms

Learn how to build a setup that will help you predict a battery's performance as it ages using a Trinket M0 and software algorithms.


Projects Dec 09, 2019 by Aaron Hanson
An Introduction to Training Theory for Neural Networks

An Introduction to Training Theory for Neural Networks

In this article, we’ll explore Perceptron training from a more theoretical perspective, focusing on the “error bowl.”