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diff --git a/src/lib/src/neuralnet_impl.h b/src/lib/src/neuralnet_impl.h
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+#pragma once
+
+#include <neuralnet/matrix.h>
+
+/// Neural network object.
+///
+/// We store the transposes of the weight matrices so that we can do forward
+/// passes with a minimal amount of work. That is, if in paper we write:
+///
+///   [w11 w21]
+///   [w12 w22]
+///
+/// then the weight matrix in memory is stored as the following array:
+///
+///   w11 w12 w21 w22
+typedef struct nnNeuralNetwork {
+  int num_layers;             // Number of non-input layers (hidden + output).
+  nnMatrix* weights;          // One matrix per non-input layer.
+  nnMatrix* biases;           // One vector per non-input layer.
+  nnActivation* activations;  // One per non-input layer.
+} nnNeuralNetwork;
+
+/// A query object that holds all the memory necessary to query a network.
+///
+/// |layer_outputs| is an array of matrices of intermediate layer outputs. There
+/// is one matrix per intermediate layer. Each matrix holds the layer's output,
+/// with one row per input, and as many columns as the layer's output size (the
+/// output vector is transposed.)
+///
+/// |network_outputs| points to the last output matrix in |layer_outputs| for
+/// convenience.
+typedef struct nnQueryObject {
+  int       num_layers;
+  nnMatrix* layer_outputs;    // Output matrices, one output per layer.
+  nnMatrix* network_outputs;  // Points to the last output matrix.
+} nnTrainingQueryObject;