MLFotoFun

MLFotoFun 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 using one of eight state of the art models. The classification includes the two most probable descriptive labels, as well as the probability associated with each label.

The eight models include: AgeNet (that classifies the age of the human subject); GenderNet (that classifies the gender of the subject); CNN Emotions (that classifies the emotion of the person); VisualSentiment (that classifies the human subject’s sentiment as positive or negative); Food101 (that classifies the food), Oxford102 (that classifies flowers); CarRecognition (that classifies the make of car); and GoogLeNetPlaces (that classifies the category of place in the image).

This app is for entertainment purposes only, and clearly demonstrates how bad such models can be, as well as the biases that they may contain, so no offence is intended with age or gender classification. It may however also surprise you in how far image recognition has come in the five years.

StatsNoisy

This app calculates the probabilities of certain performance outcomes in investment management, based on some simplifying assumptions.

It calculates the probability of a single manage outperforming the benchmark over various time periods (from 1 month to 20 years), given a user selected manager skill level (given by the Information Ratio).

It also calculates the joint probability of none, or at least one manager, underperforming the benchmark. The user can select the skill level (equal for all the managers), the time period, and the number of managers.

It also calculates the probability of a single manager outperforming the benchmark by a certain alpha target (user selected) given a user selected tracking error, under the assumption that the manager has no skill i.e. an Information Ratio of 0.

It now includes probability density and mass functions, to help visualise the distributions of the outcomes i.e. the probabilities.