Introduction Government Acquisition Teams (and Academics) request written responses to gauge writers' capacity for difficult work. Good writing requires effort. Writers must provide clear, focused, and valuable prose to Readers. Writers must do...
Sealed and Graded Video Game Collecting skyrocketed in popularity over the past decade. I joined the hobby in 2020 and stuck through the boom times of 2021 and the recent crash of 2023. Background Despite the recent crash, sealed video games do...
When I ask ChatGPT to write detailed technical content, I find that it outputs hollow, bombastic, and meandering prose. Such output places a high cognitive load on my mind: My subconscious must fill in the holes of the passive, cliche-ridden, and...
Today I collect and organize useful data visualization (Data Viz) tools that aid data exploration. I illustrate the use of the tools via the classic Abalone database, hosted on the University of California, Irvine (UCI) Machine Learning...
I've previously discussed the Reduced Coloumb Energy Neural Net algorithm on this site. I wrote the algorithm in Matlab, which uses index based logic to select, filter, wrangle and process data. Today I will refactor the Matlab code to Tidyverse....
I augment traditional literary analysis with Natural Language Processing (NLP) tools to compare Fyodor Dostoevsky's Rodion Raskolnikov (Crime and Punishment) with Ayn Rand's Howard Roark (The Fountainhead). Tools include both the Google Cloud...
The domain of Natural Language Processing (NLP) provides novel tools for Literary analysis. Analysts use NLP tools to record sentiment, emotional intensity, and word frequencies. Today I use NLP techniques to extract Raskolnikov's speaking (and...
In May 2023 Generative AI still disappoints in writing compelling literary analysis "from scratch." The output reads thin and lacks hard evidence. Today I instead measure Generative AI's effectiveness in assisting with literary analysis. I...
In this blog post I write an entry for the (now defunct) Faux Faulkner Contest. I first write Humpty Dumpty in the voice of William Faulkner. I then command Generative AI to write an entry for the contest and compare the results. You can read my...
Amazon Web Services (AWS) SageMaker Notebook Instances provide fully managed Jupyter Notebooks, tailored for Data Science and Machine Learning (ML) use cases. These notebooks allow Data Scientists and ML Engineers to explore, operationalize and...
I use the new Jasper Artificial Intelligence (AI) Art service to create the pictures in this blog post. Jasper AI Art (non-affiliate link), for example, creates the following picture of the World's Largest Turnip. Jasper generates a before and...
Machine Learning (ML) Engineers at Jasper Artificial Intelligence (AI) (non-affiliate link) trained a Natural Language Processing (NLP) model on 10% of the written content on the Internet to create a service that writes text on command. A user...
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...
Good Vs. Evil - Two Opposing paths Taken by a Similar Genius This blog post provides a comparison between Henry David Thoreau's Walden and Ted Kaczynski's Unabomber Manifesto. To compare these two works, I use both a modern Natural Language...
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...
FastAI provides Jupyter notebooks to wrangle data, train models, optimize models and then serve models. I recommended FastAI to my Data Scientist friends and they found the FastAI Jupyter layout and workflow both cumbersome and confusing. GCP...
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...