ML & Data-Science Tools
Free, hands-on tools for every ML learner — pick an algorithm, size a dataset, read model accuracy, budget GPU memory, count neural-network parameters, and estimate training time.
ML Algorithm Selector
Answer four quick questions — task type, whether your data is labeled, dataset size, and how explainable it must be — and get a beginner-friendly shortlist of algorithms to try first
Dataset Size Calculator
Estimate how much memory a dataset takes from its samples, features, and numeric precision, and split the sample count cleanly into train, validation, and test sets
Model Accuracy Predictor
Turn a confusion matrix into the metrics that matter — accuracy, precision, recall, F1 score, and specificity — to understand how well a classifier truly performs
GPU Memory Calculator
Estimate the VRAM training needs from parameter count, batch size, precision, and optimizer — with parameters, gradients, optimizer state, and activations broken out
Neural Network Calculator
Count the trainable parameters of a fully-connected network from its layer sizes — total weights, biases, parameters, and connections, computed layer by layer
Training Time Estimator
Project how long training will run from dataset size, epochs, batch size, and hardware throughput — batches per epoch, total batches, and wall-clock hours and minutes