diff --git a/live-0011/tfjs-linear-regressor.html b/live-0011/tfjs-linear-regressor.html
index bf34960..df35cb6 100644
--- a/live-0011/tfjs-linear-regressor.html
+++ b/live-0011/tfjs-linear-regressor.html
@@ -19,35 +19,13 @@ <h1>🧠 Training a Linear Regressor in TF.js</h1>
     const name = "Nono";
     console.log(`JavaScript & ${name} script says hello! 👋`);
     
-    // Define our TensorFlow model
-    const model = tf.sequential({
-        layers: [
-            tf.layers.dense({units: 1, inputShape: [1]})
-        ]
-    });
-
+    // Define your TensorFlow.s model
+    const model = tf.sequential({ layers: [ tf.layers.dense({units: 1, inputShape: [1]}) ] });
+    // Compile it
     model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
-
-    const onEpochEnd = (epoch, logs) => {
-        console.log(`Epoch ${epoch} Loss: ${logs.loss}`);
-        model.predict(tf.tensor2d([5], [1, 1])).print();
-    }
-
-    const xs = tf.tensor2d([1, 2, 3, 4, 12, -10], [6, 1]);
-    const ys = tf.tensor2d([1, 3, 5, 7, 23, -21], [6, 1]);
-
-    // Train the model using the data.
-    console.log(`Training the model..`);
-
-    const config = {
-        callbacks: {onEpochEnd},
-        epochs: 300
-    };
-
-    model.fit(xs, ys, config).then(() => {
-        model.predict(tf.tensor2d([5], [1, 1])).print();
-        console.log(`Epoch`);
-    });
+    // And train!
+    model.fit(xs, ys, config);
+    
     console.log(`Done!`);
 
     // Sample function to predict on the console