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+# pip3 install tensorflow
+
+
+import tensorflow as tf
+print("TensorFlow version:", tf.__version__)
+
+
+# Load and prepare the MNIST dataset. The pixel values of the images range from 0 through 255.
+# Scale these values to a range of 0 to 1 by dividing the values by 255.0.
+# This also converts the sample data from integers to floating-point numbers:
+mnist = tf.keras.datasets.mnist
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+
+# Build a tf.keras.Sequential model:
+model = tf.keras.models.Sequential([
+  tf.keras.layers.Flatten(input_shape=(28, 28)),
+  tf.keras.layers.Dense(128, activation='relu'),
+  tf.keras.layers.Dropout(0.2),
+  tf.keras.layers.Dense(10)
+])
+
+
+# For each example, the model returns a vector of logits or log-odds scores, one for each class.
+predictions = model(x_train[:1]).numpy()
+predictions
+
+
+# The tf.nn.softmax function converts these logits to probabilities for each class: 
+tf.nn.softmax(predictions).numpy()
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