Add troubleshooting for busy kernel jupyter notebook for Windows 😄

README.md

@@ -15,19 +15,19 @@ This course introduces you to deep learning: the state-of-the-art approach to bu
 
 Course runs July 21, 2016 - December 31, 2016
 
-## Session 1: Introduction To Tensorflow (July 21, 2016) 
+## Session 1: Introduction To Tensorflow (July 21, 2016)
 We'll cover the importance of data with machine and deep learning algorithms, the basics of creating a dataset, how to preprocess datasets, then jump into Tensorflow, a library for creating computational graphs built by Google Research. We'll learn the basic components of Tensorflow and see how to use it to filter images.
 
-## Session 2: Training A Network W/ Tensorflow (August 2, 2016) 
+## Session 2: Training A Network W/ Tensorflow (August 2, 2016)
 We'll see how neural networks work, how they are "trained", and see the basic components of training a neural network. We'll then build our first neural network and use it for a fun application of teaching a neural network how to paint an image, and explore such a network can be extended to produce different aesthetics.
 
-## Session 3: Unsupervised And Supervised Learning (August 18, 2016) 
+## Session 3: Unsupervised And Supervised Learning (August 18, 2016)
 We explore deep neural networks capable of encoding a large dataset, and see how we can use this encoding to explore "latent" dimensions of a dataset or for generating entirely new content. We'll see what this means, how "autoencoders" can be built, and learn a lot of state-of-the-art extensions that make them incredibly powerful. We'll also learn about another type of model that performs discriminative learning and see how this can be used to predict labels of an image.
 
-## Session 4: Visualizing And Hallucinating Representations (August 30, 2016) 
+## Session 4: Visualizing And Hallucinating Representations (August 30, 2016)
 This sessions works with state of the art networks and sees how to understand what "representations" they learn. We'll see how this process actually allows us to perform some really fun visualizations including "Deep Dream" which can produce infinite generative fractals, or "Style Net" which allows us to combine the content of one image and the style of another to produce widely different painterly aesthetics automatically.
 
-## Session 5: Generative Models (September 13, 2016) 
+## Session 5: Generative Models (September 13, 2016)
 The last session offers a teaser into some of the future directions of generative modeling, including some state of the art models such as the "generative adversarial network", and its implementation within a "variational autoencoder", which allows for some of the best encodings and generative modeling of datasets that currently exist. We also see how to begin to model time, and give neural networks memory by creating "recurrent neural networks" and see how to use such networks to create entirely generative text.
 
 # Github Contents Overview
@@ -176,7 +176,7 @@ $ pip3 install ipython[notebook]
 $ jupyter notebook
 ```
 
-If you run into issues that say something such as: 
+If you run into issues that say something such as:
 
 ```
 [W 20:37:40.543 NotebookApp] Kernel not found: None
@@ -437,6 +437,18 @@ SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed
 
 Solution: Download the wheel manually via curl or wget, and pip install locally.
 
+### Jupyter Notebook Kernel is always busy (Windows)
+If your have installed Docker Toolbox on Windows but your jupyter notebook doesn't run properly (the notebook kernel keeps busy all the time when you open any file) then you might need to try different browsers (One guy tried Edge and it solved his problem after struggling for long time on Chrome/Firefox).
+
+And you should also enable port forwarding by:
+1. Open VirtualBox
+2. Click on your default docker image.
+3. Click Settings.
+4. Click Network.
+5. Click forward port.
+6. Add a new rule named jupyter with host ip=127.0.0.1 and host/guess port=8888
+7. Now you should be able to browse your notebook app via localhost:8888 (instead of having to browse 192.168.xx.xx:8888)
+
 ### Something Else!
 
 Post on the [Forums](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow-i/forums?sort=recent_activity) or check on the Tensorflow [README](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md#pip-installation)