Idiot's Guide (written by an actual Idiot)

I still haven't told you what a neural net is or how one works. (Don't worry. We'll get there soon.)

You may be itching to play around with one all the same.

Unfortunately (or maybe fortunately...) you can't build a neural net with paper clips and string in your living room. And I wouldn't recommend writing much code from scratch either, even if you're already an amazing programmer (I'm not).

Luckily, some actual amazing programmers have already done all the hard work for you. Unluckily, finding which people have done it best and actually understanding how to get your hands on what they've done can sometimes feel harder than just doing the damn thing yourself.

(I don't recommend doing the damn thing yourself.)

The purpose of this post is to record roughly what I did to get myself up and running. Maybe provide a link or two or three. The post is almost entirely selfish, because I know I will never remember how to do this unless I write it down somewhere. May as well write it somewhere where it has a chance of helping others too.

User beware: I have close to zero programming experience and even less experience mucking around with Unix (besides using pine as an undergrad--ugh, dates me terribly, I know...). I can't guarantee that the steps below are maximally efficient or even close. All I know is that I can do what I need to do on my computer now. And the recipe I followed is below.

Ingredient list:

1) Python: This is the programming language everything is written in. Don't know anything about this language? That's OK. There are a buttloads of resources on-line for learning the syntax and basic structure, etc. When I'm writing a program and google a python syntax question, I often end up here. I also spent a good amount of time at the beginning going through this set of lecture notes. Or you could take one of the 10,000 MOOCs on it if you want more direction.
2) Jupyter (iPython) Notebook: This is a web-based environment that will allow you to write python code and execute it in the notebook so you can troubleshoot your code while you're writing it instead of after you've done it all wrong.
3) TensorFlow: This is the machine learning package developed by Google. People seem to like it! As do I, so far!
4) Keras: This is a front end for TensorFlow (it can also work with Theano as a back end--this is an alternative to TensorFlow that I have heard good things about but never used) that is (precisely) one zillion times more user-friendly. I was dreading building a neural net using TensorFlow until someone (Mark Hughes) told me about Keras.


Instructions (for a Mac, which is what I have):

0) Open a Terminal (look in your Applications folder if you don't know what I mean)
1) Install Anaconda (a python distribution with a package manager called "conda" that can be used to install various other packages) using the link in the "Anaconda Installation instructions" found on the TensorFlow website here. Keep in mind that there are lots of different versions of python, and the syntax actually varies quite a bit (annoyingly) among them. I went ahead and got the most recent version of python 3.
2) Create a conda environment called tensorflow by typing (where you may replace "3.5" with the python version number you are using):

      $ conda create -n tensorflow python=3.5

3) Activate the tensorflow environment by typing:

$ source activate tensorflow 

(tensorflow)$  # Your prompt should change

4) Install tensorflow using conda:

# Linux/Mac OS X, Python 2.7/3.4/3.5, CPU only:
(tensorflow)$ conda install -c conda-forge tensorflow

5) At this point, I followed an amazingly helpful answer on Quora found here to the question "How can I work with Keras on a Jupyter notebook with TensorFlow as a backend?" One needs to install ipython, Jupyter, and Keras (in that order) inside the tensorflow environment by typing:

(tensorflow) username$ conda install ipython
(tensorflow) username$ pip install jupyter
(tensorflow) username$ pip install keras 

6) Now deactivate and reactivate the tensorflow environment (not sure why you need to do this):

1. 
   
2. (tensorflow)username$ source deactivate tensorflow
3. username$ source activate tensorflow

7)  And open the Jupyter notebook (it will open in a browser: I think Safari is the default, at least that's what opens on my computer):

(tensorflow)username$ jupyter notebook

8)  Once the Jupyter notebook opens, navigate to whichever directory you want to use to store your ipython notebooks and click (upper right) New-->Python3 and you'll be in a Jupyter notebook. You can execute python code by doing a Shift-Return. You might try writing "print('hello, world')" to make sure you've got it. (It should output "hello, world" obvs).

9) To shut everything down, save your notebook, close the browser window, do a Ctrl-c at your terminal and answer "Y" when it asks you whether you want to shut down your Jupyter notebook.

10) You'll be back at the tensorflow prompt, at which point you deactivate tensorflow environment:

1. (tensorflow)username$ source deactivate tensorflow

11) Type "Exit" to close your terminal and you're all done.

12) Now whenever you want to open a Jupyter Notebook and use Keras, TensorFlow you just do:

$ source activate tensorflow 

(tensorflow)$  # Your prompt should change

 then

(tensorflow)username$ jupyter notebook

and once you're all finished with your jupyter notebook:

(tensorflow)username$ source deactivate tensorflow

13) BTW, here's the documentation for Keras. I followed this great tutorial to understand how to build an LSTM (a particular neural network architecture that is good for analyzing sequences--will write more about this later) using Keras. Really cool thing I didn't realize until I did this tutorial: you can use Keras to download interesting data sets (this tutorial uses some imdb movie reviews and classifies them into "positive" or "negative")--I haven't explored which ones.