Organize folders, add notebook readme

Deep_Art/deepdream/README.md

@@ -21,7 +21,7 @@ $ scp -i <pem_file> <files_to_be_copied> ubuntu@<aws_pub_ip_or_dns>P
 
 ### Checking the Instance
 
-* The root directory is only 12GB, and only ~ 3GB of that is free. 
+* The root directory is only 12GB, and only ~3GB of that is free. 
 
 * There should be a 60GB /mnt directory that you can use to put your data, model checkpoints, models etc. Remember that the /mnt directory won’t be persistent across reboots/terminations.
 
@@ -58,6 +58,7 @@ $ ssh -i <pem_file> -N -f -L localhost:8888:localhost:8888 ubuntu@<aws_pub_ip_or
 ```
 
 Note: add your AWS key to your ```~/.ssh/config``` file to avoid having to type it in all the time.
+Note 2: kill the process later, finding it with ```ps aux | grep localhost:8888```.
 
 
 ## Running it in a Dockerfile

Notebooks/README.md

@@ -0,0 +1,15 @@
+## Jupyter Notebooks
+
+### Installing
+
+Install any dependences for [Jupyter](http://jupyter.readthedocs.io/en/latest/install.html):
+
+```shell
+$ apt-get install build-essential python3-dev
+$ pip install jupyter
+```
+
+
+### Basics
+
+* A notebook is made up of a number of cells with Python code. You can execute a cell by clicking on it and pressing ```Shift-Enter```.

README.md

@@ -1,6 +1,6 @@
 # Machine Learning Resources
 
-
+Just a place where I dump ML+DL+AI stuff.
 
 ## Online courses:
 
@@ -12,10 +12,6 @@
 
 ## Resources:
 
-* Check [papers](https://github.com/bt3gl/Machine-Learning-Resources/tree/master/Papers) folder.
-* Check [talks](https://github.com/bt3gl/Machine-Learning-Resources/tree/master/Talks) folder.
-* Check [docker images](https://github.com/bt3gl/Machine-Learning-Resources/tree/master/Code/Docker_Images) folder.
-* Check [ipython notebooks](https://github.com/bt3gl/Machine-Learning-Resources/tree/master/Notebooks) folder.
 * Check [my blog](http://bt3gl.github.io/) :).
 * Feedly with [list of blogs to follow](https://raw.githubusercontent.com/bt3gl/Machine-Learning-Resources/master/ml_ai_feedly.opml).