Quickstart: from data to prediction in ~10 lines

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The shortest possible path with ggmlR: take a built-in dataset, train a neural network, and predict — using only the core Keras-like API, no tidymodels or mlr3. (For those ecosystems see the tidymodels and mlr3 vignettes.)

library(ggmlR)

x <- scale(as.matrix(iris[, 1:4]))            # 4 numeric features
y <- model.matrix(~ Species - 1, iris)        # one-hot, 3 classes

model <- ggml_model_sequential() |>
  ggml_layer_dense(16L, activation = "relu", input_shape = 4L) |>
  ggml_layer_dense(3L,  activation = "softmax") |>
  ggml_compile(optimizer = "adam", loss = "categorical_crossentropy")

model <- ggml_fit(model, x, y, epochs = 100L, verbose = 0L)

pred  <- ggml_predict(model, x)               # [150 x 3] class probabilities
acc   <- mean(max.col(pred) == as.integer(iris$Species))
cat(sprintf("accuracy: %.3f\n", acc))

That’s it — load data, stack layers, ggml_compile(), ggml_fit(), ggml_predict(). ggmlR runs on the GPU via Vulkan automatically when available and falls back to the CPU otherwise; call ggml_model_backend(model) to see which backend was actually used.

Next steps

Keras-like API vignette — full sequential & functional models, layers, callbacks, save/load.
Autograd engine vignette — PyTorch-style dynamic graphs (ag_*).
tidymodels / mlr3 vignettes — use ggmlR as an engine/learner inside those ecosystems.
GPU / Vulkan vignette — device selection and performance.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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