Fixed tutorial warnings

docs/tutorials/gluon/hybrid.md

@@ -154,7 +154,10 @@ You can use other language bindings to load them. You can also load them back
 to gluon with `SymbolBlock`:
 
 ```python
-net2 = gluon.SymbolBlock.imports('model-symbol.json', ['data'], 'model-0001.params')
+import warnings
+
+with warnings.catch_warnings():
+    net2 = gluon.SymbolBlock.imports('model-symbol.json', ['data'], 'model-0001.params')
 ```
 
 ## Operators that do not work with hybridize
@@ -259,4 +262,4 @@ For example, avoid writing `x += y` and use `x  = x + y`, otherwise you will get
 
 The recommended practice is to utilize the flexibility of imperative NDArray API during experimentation. Once you finalized your model, make necessary changes mentioned above so you can call `hybridize` function to improve performance.
 
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->

docs/tutorials/gluon/save_load_params.md

@@ -260,7 +260,9 @@ One of the main reasons to serialize model architecture into a JSON file is to l
 Serialized Hybrid networks (saved as .JSON and .params file) can be loaded and used inside Python frontend using `gluon.nn.SymbolBlock`. To demonstrate that, let's load the network we serialized above.
 
 ```python
-deserialized_net = gluon.nn.SymbolBlock.imports("lenet-symbol.json", ['data'], "lenet-0001.params", ctx=ctx)
+import warnings
+with warnings.catch_warnings():
+    deserialized_net = gluon.nn.SymbolBlock.imports("lenet-symbol.json", ['data'], "lenet-0001.params", ctx=ctx)
 ```
 
 `deserialized_net` now contains the network we deserialized from files. Let's test the deserialized network to make sure it works.

docs/tutorials/index.md

@@ -134,7 +134,6 @@ Select API: 
     * [MNIST Handwritten Digit Classification](/tutorials/python/mnist.html)
     * [Movie Review Classification using Convolutional Networks](/tutorials/nlp/cnn.html)
     * [Generative Adversarial Networks (GANs)](/tutorials/unsupervised_learning/gan.html)
-    * [Recommender Systems using Matrix Factorization](/tutorials/python/matrix_factorization.html)
     * [Speech Recognition with Connectionist Temporal Classification Loss](/tutorials/speech_recognition/ctc.html)
 * Practitioner Guides
     * [Predicting on new images using a pre-trained ImageNet model](/tutorials/python/predict_image.html)

docs/tutorials/onnx/fine_tuning_gluon.md

@@ -279,7 +279,9 @@ We create a symbol block that is going to hold all our pre-trained layers, and a
 
 
 ```python
-pre_trained = gluon.nn.SymbolBlock(outputs=new_sym, inputs=mx.sym.var('data_0'))
+import warnings
+with warnings.catch_warnings():
+    pre_trained = gluon.nn.SymbolBlock(outputs=new_sym, inputs=mx.sym.var('data_0'))
 net_params = pre_trained.collect_params()
 for param in new_arg_params:
     if param in net_params:

docs/tutorials/onnx/inference_on_onnx_model.md

@@ -144,7 +144,9 @@ print(data_names)
 And load them into a MXNet Gluon symbol block. 
 
 ```python
-net = gluon.nn.SymbolBlock(outputs=sym, inputs=mx.sym.var('data_0'))
+import warnings
+with warnings.catch_warnings():
+    net = gluon.nn.SymbolBlock(outputs=sym, inputs=mx.sym.var('data_0'))
 net_params = net.collect_params()
 for param in arg_params:
     if param in net_params:
@@ -247,6 +249,7 @@ Lucky for us, the [Caltech101 dataset](http://www.vision.caltech.edu/Image_Datas
 
 We show that in our next tutorial:
 
+
 - [Fine-tuning an ONNX Model using the modern imperative MXNet/Gluon](http://mxnet.incubator.apache.org/tutorials/onnx/fine_tuning_gluon.html)
 
 <!-- INSERT SOURCE DOWNLOAD BUTTONS -->

tests/tutorials/test_tutorials.py

@@ -139,9 +139,6 @@ def test_onnx_fine_tuning_gluon():
 def test_onnx_inference_on_onnx_model():
     assert _test_tutorial_nb('onnx/inference_on_onnx_model')
 
-def test_python_matrix_factorization():
-    assert _test_tutorial_nb('python/matrix_factorization')
-
 def test_python_linear_regression():
     assert _test_tutorial_nb('python/linear-regression')