Tag: Probability

Distributions (Statistical)

Distributions is a visual guide to probability. Explore PDFs/PMFs and CDFs with live sliders, see how parameters reshape curves and tails, and build intuition with stats at a glance. It’s a calm, hands‑on way to make abstract ideas click.

Highlights

– Interactive charts for mass/density and cumulative functions

– Live parameter editing with sliders and text fields

– Mean, variance, skewness, and more—updated in real time

– Random sampling demo to see variability in action

– Examples and presets to jumpstart exploration

– Dark mode support

Coverage

– Discrete (finite): Bernoulli, Binomial, Discrete Uniform, Hypergeometric, Rademacher

– Discrete (infinite): Borel, Geometric, Negative Binomial, Poisson

– Continuous (finite): Beta, Uniform, Kumaraswamy

– Continuous (semi‑infinite): Chi‑squared, Exponential, F, Gamma, LogNormal, Pareto, Weibull

– Continuous (infinite): Cauchy, Laplace, Logistic, Normal, Standard Normal, Student t

What’s new

– More distributions and parameter presets

– Smoother charts, clearer statistics, and improved sampling demo

– UI and dark mode refinements, plus updated help

Who it’s for

– Students and teachers

– Analysts and anyone who learns best by seeing and tweaking

Copyright © 2025 Joao Frasco. All rights reserved.

Photo Classifier

Photo Classifier uses state of the art Machine Learning models that use Convolutional Neural Networks (CNNs) to classify photos taken with your camera or from your library. The classification includes both a descriptive label of the scene, as well as the probability associated with the label.

Six models have been included, and the app provides the output from each of the models in a fraction of a second each. You can see more detail on the top five predictions for each model, along with the probability of each prediction.

This is a simple but very powerful tool that demonstrates how far artificial intelligence and machine learning has come, especially the power of deep neural networks and specifically, convolutional neural networks. These models are all freely available from the internet under the licenses provided by the links below.

GoogLeNetPlaces: Creative Common License. More information available at http://places.csail.mit.edu

Resnet50: MIT License. More information available at https://github.com/fchollet/keras/blob/master/LICENSE

Inceptionv3: MIT License. More information available at https://github.com/fchollet/keras/blob/master/LICENSE

VGG16: Creative Commons Attribution 4.0 International(CC BY 4.0). More information available at https://creativecommons.org/licenses/by/4.0/

SqueezeNet: BSD License. More information available at https://github.com/DeepScale/SqueezeNet/blob/master/LICENSE

MobileNet: Apache License. Version 2.0 http://www.apache.org/licenses/LICENSE-2.0