A question. A few lines.
A place to begin.
Explore real Aner programs, from a first variable to a neural classifier. Download the source and follow every step.
An Iris classifier in a few lines
Split first, fit preprocessing on training rows, and evaluate a small dense classifier on held out data.
Explore the program Neural networksClassifying Wine measurements
Use the same concise pipeline with 13 features and the attributed UCI Wine recognition dataset.
Explore the program DataFrom a typed CSV to a tensor
Validate synthetic records, preserve missingness, summarize bounded batches, and explicitly collect complete rows.
Explore the program Machine learningKNN on Iris
Standardize training data, fit a nearest neighbor classifier, and inspect held out predictions.
Explore the program Machine learningClustering Iris
Fit K means and evaluate clusters without assuming that cluster IDs equal class labels.
Explore the program VisualizationSee a network learn XOR
Capture actual training steps for an offline topology, loss, and gradient report.
Explore the program LanguageA binary search tree
Use recursive insertion and lookup with typed nullable nodes.
Explore the program LanguageA cyclic graph
Explore object identity and cyclic references in Aner’s managed object heap.
Explore the program LanguageA linked list
Build nullable links and use methods to traverse and mutate a reference structure.
Explore the program LanguageConcise generic classes
Use private fields, implicit receivers, and inferred result types without repetitive method syntax.
Explore the program DataExplore the teaching catalogue
Inspect offline datasets, dimensions, citations, licenses, and source versions.
Explore the program LanguageFunctions and control flow
Use recursion, conditions, loops, and local scope.
Explore the program LanguageGeneric linked structures
Reuse Node<T> and LinkedList<T> for integers, floating point values, and Employee objects.
Explore the program LanguageHello, Aner
Start with scalar arithmetic in a complete Aner program.
Explore the program Neural networksIris: the full training loop
Inspect explicit parameters, softmax cross entropy, SGD, and held out evaluation.
Explore the program Neural networksLearning XOR
Train a small dense network on synthetic XOR data with native reverse mode differentiation.
Explore the program LanguageTypes and arithmetic
Explore typed bindings, strings, booleans, comparisons, and numerical expressions.
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