diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index b1e2404..4da1f2b 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -14,5 +14,6 @@ endfunction(build_example)
 # build_example(FFNet.cpp)
 # build_example(Node.cpp)
 build_example(perceptron.cpp)
+build_example(alexnet.cpp)
 # build_example(Weights.cpp)
 build_example(autograd.cpp)
diff --git a/examples/alexnet.cpp b/examples/alexnet.cpp
new file mode 100644
index 0000000..fb415cc
--- /dev/null
+++ b/examples/alexnet.cpp
@@ -0,0 +1,78 @@
+/*******************************************************
+ * Copyright (c) 2017, ArrayFire
+ * All rights reserved.
+ *
+ * This file is distributed under 3-clause BSD license.
+ * The complete license agreement can be obtained at:
+ * http://arrayfire.com/licenses/BSD-3-Clause
+ ********************************************************/
+
+#include <af/autograd.h>
+#include <af/nn.h>
+
+using namespace af;
+using namespace af::nn;
+using namespace af::autograd;
+
+int main()
+{
+    const int inputSize  = 2;
+    const int outputSize = 1;
+    const double lr = 0.1;
+    const int numSamples = 1;
+
+    auto in = af::randu(227, 227, 3, numSamples);
+    auto out = af::randu(55, 55, 96, 1);
+
+    nn::Sequential alexnet;
+
+    //alexnet.add(nn::Conv2D(11, 11, 4, 4, 0, 0, 3, 96, true));
+    alexnet.add(nn::ReLU());
+
+    Variable result;
+    for (int i = 0; i < 1000; i++) {
+        for (int j = 0; j < numSamples; j++) {
+            alexnet.train();
+            alexnet.zeroGrad();
+
+            af::array in_j = in(af::span, af::span, af::span, j);
+            af::array out_j = out;
+
+            // Forward propagation
+            result = alexnet.forward(nn::input(in_j));
+
+            // Calculate loss
+            // TODO: Use loss function
+            af::array diff = out_j - result.array();
+
+            // Backward propagation
+            auto d_result = Variable(diff, false);
+            result.backward(d_result);
+
+            // Update parameters
+            // TODO: Should use optimizer
+            for (auto &param : alexnet.parameters()) {
+                param.array() += lr * param.grad().array();
+                param.array().eval();
+            }
+        }
+
+        if ((i + 1) % 100 == 0) {
+            alexnet.eval();
+
+            // Forward propagation
+            result = alexnet.forward(nn::input(in));
+
+            // Calculate loss
+            // TODO: Use loss function
+            af::array diff = out - result.array();
+            printf("Average Error at iteration(%d) : %lf\n", i + 1, af::mean<float>(af::abs(diff)));
+            printf("Predicted\n");
+            //af_print(result.array());
+            printf("Expected\n");
+            //af_print(out);
+            printf("\n\n");
+        }
+    }
+    return 0;
+}