How to do it...

1. At the moment, gluon is included in the latest release of MXNet (follow the steps in Building efficient models with MXNet to install MXNet). 
2. After installing, we can directly import gluon as follows:

from mxnet import gluon

1. Next, we create some dummy data. For this we need the data to be in MXNet's NDArray or Symbol:

import mxnet as mx
import numpy as np
x_input = mx.nd.empty((1, 5), mx.gpu())
x_input[:] = np.array([[1,2,3,4,5]], np.float32)

y_input = mx.nd.empty((1, 5), mx.gpu())
y_input[:] = np.array([[10, 15, 20, 22.5, 25]], np.float32)

1. With Gluon, it's really straightforward to build a neural network by stacking layers:

net = gluon.nn.Sequential()
with net.name_scope():
    net.add(gluon.nn.Dense(16, activation="relu"))
    net.add(gluon.nn.Dense(len(y_input)))

1. Next, we initialize the parameters and we store these on our GPU as follows:

net.collect_params().initialize(mx.init.Normal(), ctx=mx.gpu())

1. With the following code we set the loss function and the optimizer:

softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': .1})

1. We're ready to start training or model:

n_epochs = 10

for e in range(n_epochs):
    for i in range(len(x_input)):
        input = x_input[i]
        target = y_input[i]
        with mx.autograd.record():
            output = net(input)
            loss = softmax_cross_entropy(output, target)
            loss.backward()
        trainer.step(input.shape[0])