diff --git a/jupyter/TrainingUtils.java b/jupyter/TrainingUtils.java
new file mode 100644
index 00000000000..1a5ea729e95
--- /dev/null
+++ b/jupyter/TrainingUtils.java
@@ -0,0 +1,85 @@
+import ai.djl.Model;
+import ai.djl.training.Trainer;
+import ai.djl.training.TrainingListener;
+import ai.djl.training.dataset.Batch;
+import ai.djl.training.dataset.Dataset;
+import ai.djl.training.util.ProgressBar;
+import java.io.IOException;
+import java.nio.file.Paths;
+
+public class TrainingUtils {
+
+    public static void fit(
+            Trainer trainer,
+            int numEpoch,
+            Dataset trainingDataset,
+            Dataset validateDataset,
+            String outputDir,
+            String modelName)
+            throws IOException {
+        for (int epoch = 0; epoch < numEpoch; epoch++) {
+            for (Batch batch : trainer.iterateDataset(trainingDataset)) {
+                trainer.trainBatch(batch);
+                trainer.step();
+                batch.close();
+            }
+
+            if (validateDataset != null) {
+                for (Batch batch : trainer.iterateDataset(validateDataset)) {
+                    trainer.validateBatch(batch);
+                    batch.close();
+                }
+            }
+            // reset training and validation metric at end of epoch
+            trainer.resetTrainingMetrics();
+            // save model at end of each epoch
+            if (outputDir != null) {
+                Model model = trainer.getModel();
+                model.setProperty("Epoch", String.valueOf(epoch));
+                model.save(Paths.get(outputDir), modelName);
+            }
+        }
+    }
+
+    public static TrainingListener getTrainingListener(
+            ProgressBar trainingProgressBar, ProgressBar validateProgressBar) {
+        return new SimpleTrainingListener(trainingProgressBar, validateProgressBar);
+    }
+
+    private static final class SimpleTrainingListener implements TrainingListener {
+
+        private ProgressBar trainingProgressBar;
+        private ProgressBar validateProgressBar;
+        private int trainingProgress;
+        private int validateProgress;
+
+        public SimpleTrainingListener(
+                ProgressBar trainingProgressBar, ProgressBar validateProgressBar) {
+            this.trainingProgressBar = trainingProgressBar;
+            this.validateProgressBar = validateProgressBar;
+        }
+
+        /** {@inheritDoc} */
+        @Override
+        public void onTrainingBatch() {
+            if (trainingProgressBar != null) {
+                trainingProgressBar.update(trainingProgress++);
+            }
+        }
+
+        /** {@inheritDoc} */
+        @Override
+        public void onValidationBatch() {
+            if (validateProgressBar != null) {
+                validateProgressBar.update(validateProgress++);
+            }
+        }
+
+        /** {@inheritDoc} */
+        @Override
+        public void onEpoch() {
+            trainingProgress = 0;
+            validateProgress = 0;
+        }
+    }
+}
diff --git a/jupyter/transfer_learning_on_cifar10.ipynb b/jupyter/transfer_learning_on_cifar10.ipynb
index 96fcbfed474..028d6c011a9 100644
--- a/jupyter/transfer_learning_on_cifar10.ipynb
+++ b/jupyter/transfer_learning_on_cifar10.ipynb
@@ -36,14 +36,11 @@
     "%maven ai.djl:api:0.2.0\n",
     "%maven ai.djl:basicdataset:0.2.0\n",
     "%maven ai.djl:model-zoo:0.2.0\n",
-    "%maven ai.djl:examples:0.2.0\n",
     "%maven ai.djl:repository:0.2.0\n",
     "%maven ai.djl.mxnet:mxnet-engine:0.2.0\n",
     "%maven ai.djl.mxnet:mxnet-model-zoo:0.2.0\n",
-    "%maven org.apache.logging.log4j:log4j-slf4j-impl:2.12.1\n",
-    "%maven org.apache.logging.log4j:log4j-core:2.12.1\n",
-    "%maven org.apache.logging.log4j:log4j-api:2.12.1 \n",
     "%maven org.slf4j:slf4j-api:1.7.26\n",
+    "%maven org.slf4j:slf4j-simple:1.7.26\n",
     "%maven net.java.dev.jna:jna:5.3.0"
    ]
   },
@@ -83,7 +80,6 @@
    "source": [
     "import ai.djl.*;\n",
     "import ai.djl.basicdataset.*;\n",
-    "import ai.djl.examples.training.util.*;\n",
     "import ai.djl.modality.cv.transform.*;\n",
     "import ai.djl.mxnet.zoo.*;\n",
     "import ai.djl.ndarray.*;\n",
@@ -238,8 +234,15 @@
     "int epoch = 10;\n",
     "Trainer trainer = model.newTrainer(config);\n",
     "Shape inputShape = new Shape(1, 3, 32, 32);\n",
-    "trainer.initialize(inputShape);\n",
-    "\n",
+    "trainer.initialize(inputShape);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "float trainingAccuracy = 0f;\n",
     "for (int i = 0; i < epoch; ++i) {\n",
     "    int index = 0;\n",
@@ -284,7 +287,7 @@
    "metadata": {},
    "source": [
     "## Use the `fit` method\n",
-    "Instead of writing the two `for` loops, you can use the `fit` method in `TrainingUtils`, which will handle everything automatically. Just pass your `trainer`, number of epochs to train, training dataset, validation dataset (if any), model output path, and model name. It will save your model checkpoint at the end of each epoch."
+    "Instead of writing the two `for` loops, you can use the `fit` method in [TrainingUtils](TrainingUtils.java), which will handle everything automatically. Just pass your `trainer`, number of epochs to train, training dataset, validation dataset (if any), model output path, and model name. It will save your model checkpoint at the end of each epoch."
    ]
   },
   {
@@ -293,6 +296,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%load TrainingUtils.java\n",
+    "\n",
+    "trainer.setTrainingListener(TrainingUtils.getTrainingListener(progressBar, null));\n",
+    "\n",
     "TrainingUtils.fit(trainer, epoch, trainDataset, null, \"build/resnet\", \"resnetv1\");"
    ]
   },