This webpage trains a neural network, specifically a linear classifier, within your web browser to predict the species of an Iris flower based on its input variables. The Iris flower dataset contains the input variables (features) of Sepal Length, Sepal Width, Petal Length, and Petal Width for 150 different Iris flowers. These flowers belong to three different species: Setosa, Versicolor, and Virginica.

In the linear classifier, the input variables $(x)$ are multiplied by a matrix of weights $(W)$, and a vector $(b)$ is added to the product. This produces a set of weighted input values that represent the relative importance of each input variable in predicting the species of the flower. The index of the largest value in the resultant vector is the predicted species of the flower $(y)$. Initially, $W$ and $b$ are assigned random values, but the neural network learns by adjusting these values in a way that minimizes future error.

Error is calculated using the softmax and cross-entropy functions. The softmax function assigns a probability to each possible output value (i.e. each species of Iris flower), and the cross-entropy function measures the difference between the predicted probabilities and the true probabilities (i.e. the actual species of the flower). By minimizing this difference, the neural network improves its accuracy in correctly predicting the species of an Iris flower from its input variables.

Here is the full dataset: