Added usage example.

ONN_Usage_Example.ipynb

+%% Cell type:markdown id: tags:
+
+#Installing Dependencies
+
+%% Cell type:code id: tags:
+
+``` 
+!pip install onn
+```
+
+%% Cell type:markdown id: tags:
+
+##Importing Dependencies
+
+%% Cell type:code id: tags:
+
+``` 
+from onn.OnlineNeuralNetwork import ONN
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+import numpy as np
+```
+
+%% Cell type:markdown id: tags:
+
+## Initializing Network
+
+%% Cell type:code id: tags:
+
+``` 
+onn_network = ONN(features_size=10, max_num_hidden_layers=5, qtd_neuron_per_hidden_layer=40, n_classes=10)
+```
+
+%% Cell type:markdown id: tags:
+
+##Creating Fake Classification Dataset
+
+%% Cell type:code id: tags:
+
+``` 
+X, Y = make_classification(n_samples=50000, n_features=10, n_informative=4, n_redundant=0, n_classes=10,
+                           n_clusters_per_class=1, class_sep=3)
+X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42, shuffle=True)
+```
+
+%% Cell type:markdown id: tags:
+
+##Learning and predicting at the same time
+
+%% Cell type:code id: tags:
+
+``` 
+for i in range(len(X_train)):
+  onn_network.partial_fit(np.asarray([X_train[i, :]]), np.asarray([y_train[i]]))
+  
+  if i % 1000 == 0:
+    predictions = onn_network.predict(X_test)
+    print("Online Accuracy: {}".format(accuracy_score(y_test, predictions)))
+```