These are materials I use for various classes on deep learning. Each file is a self contained unit that demonstrates a specific thing. Downloading or cloning this repository before class is a great way to follow along.
Please feel free to use these materials for your own classes/projects etc. If you do that, I would love it if you sent me a message and let me know what you're up to.
You can find video overviews of a lot of the material at https://youtu.be/Zxrk88rA7fA.
These classes are intended for people who are comfortable wirth python.
If you are uncomfortable opening up a terminal, I strongly recommend doing a quick tutorial before you take this class. Setting up your machine can be painful but once you're setup you can get a ton out of the class. I recommend getting started ahead of time.
If you're on Windows I recommend checking out http://thepythonguru.com/.
If you're on a Mac check out http://www.macworld.co.uk/how-to/mac/coding-with-python-on-mac-3635912/
If you're on linux, you're probably already reasonably well setup :).
If you run into trouble, the book Learn Python the Hard Way has installation steps in great detail: https://learnpythonthehardway.org/book/ex0.html. It also has a refresher on using a terminal in the appendix.
If you are comfortable opening up a terminal but want a python intro/refresher check out https://www.learnpython.org/ for a really nice introduction to Python.
A lot of people like to follow along with ipython or jupyter notebooks and I think that's great! It makes data exploration easier. I also really appreciate pull requests to make the code clearer.
If you've never used pandas or numpy - they are great tools and I use them heavily in my work and for this class. I assume no knlowedge of pandas and numpy but you may want to do some learning on your own. You can get a quick overview of pandas at http://pandas.pydata.org/pandas-docs/stable/10min.html. There is a great overview of numpy at https://docs.scipy.org/doc/numpy/user/quickstart.html.
I recommend running this code in a pre-configured environment. You can rent an AWS EC2 node with any of the "Deep Learning" AMIs from aws.amazon.com or a GCP instance.
Once you have a cloud machine setup run:
pip install -r requirements.txt
You can also install this class locally, but it may be trickier.
Install git: https://git-scm.com/download/win
Install anaconda
Try running the following from the command prompt:
python --version
You should see something like
Python 3.6.1 :: Anaconda 4.4.0 (64-bit)
If don't see "Anaconda" in the output, search for "anaconda prompt" from the start menu and enter your command prompt this way. It's also best to use a virtual environment to keep your packages silo'ed. Do so with:
conda create -n ml-class python=3.6
activate ml-class
Whenever you start a new terminal, you will need to call activate ml-class.
The most common problem is an old version of python. Its easy to have multiple versions of python installed at once and Macs in particular come with a default version of python that is too old to install tensorflow.
Try running:
python --version
If your version is less than 2.7.12, you have a version issue. Try reinstalling python 2.
git clone https://github.com/lukas/ml-class.git
cd ml-class
pip install wandb
conda install -c conda-forge scikit-learn
conda install -c conda-forge tensorflow
conda install -c conda-forge keras
You can download python from https://www.python.org/downloads/. There are more detailed instructions for windows installation at https://www.howtogeek.com/197947/how-to-install-python-on-windows/.
The material should work with python 2 or 3. On Windows, you need to install thre 64 bit version of python 3.5 or 3.6 in order to install tensorflow.
git clone https://github.com/lukas/ml-class.git
cd ml-class
If you get an error message here, most likely you don't have git installed. Go to https://www.atlassian.com/git/tutorials/install-git for intructions on installing git.
pip install -r requirements.txt
To make sure your installation works go to the directory where this file is and run
python test-scikit.py
You should see the output "Scikit is installed!"
python test-keras.py
You should see the output "Using TensorFlow backend. Keras is installed!"