diff --git a/README.md b/README.md
index 092567b..1d1865a 100644
--- a/README.md
+++ b/README.md
@@ -521,6 +521,7 @@ Further resources:
 * [TensorFlow.js](https://js.tensorflow.org/) - A WebGL accelerated, browser based JavaScript library for training and deploying ML models.
 * [JSMLT](https://github.com/jsmlt/jsmlt) - Machine learning toolkit with classification and clustering for Node.js; supports visualization (see [visualml.io](https://visualml.io)).
 * [xgboost-node](https://github.com/nuanio/xgboost-node) - Run XGBoost model and make predictions in Node.js.
+* [Netron](https://github.com/lutzroeder/netron) - Visualizer for machine learning models.
 
 <a name="javascript-misc"></a>
 #### Misc
@@ -988,6 +989,7 @@ be
 * [Turi Create](https://github.com/apple/turicreate)  - Machine learning from Apple. Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.
 * [xLearn](https://github.com/aksnzhy/xlearn) - A high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data, which is very common in Internet services such as online advertisement and recommender systems.
 * [mlens](https://github.com/flennerhag/mlens) - A high performance, memory efficient, maximally parallelized ensemble learning, integrated with scikit-learn.
+* [Netron](https://github.com/lutzroeder/netron) - Visualizer for machine learning models.
 
 <a name="python-data-analysis"></a>
 #### Data Analysis / Data Visualization