Tag Neural Networks

Polars streaming avoids out-of-memory errors in large cross joins via processing data in chunks. Learn how to run 27M row workloads on a single machine.

Refactoring an RCE machine learning algorithm from Pandas lambda functions to the Polars expression API reduced execution time from six minutes to fourteen seconds. Polars cross joins, columnar operations, and Apache Arrow drive a 25x speedup.

Refactor a Reduced Coulomb Energy neural network implementation from Matlab into R Tidyverse with pipes, tibbles, functional operations, and vectorized distance calculations. Compares loop-based Matlab patterns with tidy data workflows for...

In part one of this two-part series, I developed a Reduced Columb Energy (RCE) classifier in Python. RCE calculates hit footprints around training data and uses the footprints to classify test data. RCE draws a circle around each labeled training...

In Pattern Classification Using Neural Networks (IEEE Communications Magazine, Nov. 1989) Richard P. Lippman provides the following definition of Exemplar neural net classifiers: [Exemplar classifiers] perform classification based on the identity...

Data Scientists need skill and experience to create useful Machine Learning (ML) models. ML activities include tool selection, training logistic decisions (move data to training vs. train in-situ), data acquisition, data cleaning, data quality...

I started my AI/ML journey in 2011 with a laptop model, a term which indicates a measure of size. Laptop models, by definition, do not exceed the compute, memory and storage resources of a single piece of hardware. The laptop model approach works...

Model optimization on traditional Artificial Intelligence and Machine Learning (AI/ML) platforms requires considerable Data Architect expertise and judgement. These ML platforms require the Architect to choose from dozens of available training...

In this demonstration we continue to use Keras and TensorFlow 2.3 to explore data, normalize data, and build both a linear model and Deep Neural Network (DNN) to solve a regression problem. Today we use Principal Component Analysis (PCA) to...

In this demonstration we will use Keras and TensorFlow 2.3 to explore data, normalize data, and build both a linear model and Deep Neural Network (DNN) to solve a regression problem. TensorFlow Core 2.3 includes tf.keras, which provides the high...

Introduction Machine Learning engineers use Probabilistic Neural Networks (PNN) for classification and pattern recognition tasks. PNN use a Parzen Window along with a non-negative kernel function to estimate the probability distribution function...

Introduction I investigate the effectiveness of a Reduced Coulomb Energy (RCE) Neural Network on the classification of the University of California, Irvine (UCI) Bupa liver disorder data set. I investigate seven (7) different versions of the data...