Update README.md

README.md

@@ -15,4 +15,46 @@ Make sure your compiler supports C++17. Shkyera Grad is a header-only library, s
 #include "include/ShkyeraGrad.hpp"
 ```
 
-Check out the [examples](examples/README.md) for a quick start on Shkyera Grad.
+Check out the [examples](examples/README.md) for a quick start on Shkyera Grad. In the meantime, here's a neural network that learns the XOR function.
+
+```cpp
+#include "include/ShkyeraGrad.hpp"
+
+int main() {
+    using namespace shkyera;
+
+    std::vector<Vec32> xs;
+    std::vector<Vec32> ys;
+
+    // ---------- INPUT ----------- | -------- OUTPUT --------- //
+    xs.push_back(Vec32::of({0, 0})); ys.push_back(Vec32::of({0}));
+    xs.push_back(Vec32::of({1, 0})); ys.push_back(Vec32::of({1}));
+    xs.push_back(Vec32::of({0, 1})); ys.push_back(Vec32::of({1}));
+    xs.push_back(Vec32::of({0, 0})); ys.push_back(Vec32::of({0}));
+
+    auto mlp = SequentialBuilder<Type::float32>::begin()
+                .add(Layer32::create(2, 15, Activation::relu<Type::float32>))
+                .add(Layer32::create(15, 5, Activation::relu<Type::float32>))
+                .add(Layer32::create(5, 1, Activation::sigmoid<Type::float32>))
+                .build();
+
+    Optimizer32 optimizer = Optimizer<Type::float32>(mlp->parameters(), 0.1);
+    Loss::Function32 lossFunction = Loss::MSE<Type::float32>;
+
+    // ------ TRAINING THE NETWORK ------- //
+    for (size_t epoch = 0; epoch < 100; epoch++) {
+        auto epochLoss = Val32::create(0);
+
+        optimizer.reset();
+        for (size_t sample = 0; sample < xs.size(); ++sample) {
+            Vec32 pred = mlp->forward(xs[sample]);
+            auto loss = lossFunction(pred, ys[sample]);
+
+            epochLoss = epochLoss + loss;
+        }
+        optimizer.step();
+
+        std::cout << "Epoch: " << epoch + 1 << " Loss: " << epochLoss->getValue() << std::endl;
+    }
+}
+```