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!"
##Troubleshooting on Ubuntu
If the above 2 commands fail and you see an error "ImportError no module named".. verify if you have multiple versions of python installed, specially if you are on Ubuntu version 16 or higher.
Run python --version -- may be you have v2.7? Run python3 --version -- may be you have python 3.5+?
If you have python3 installed, verify your ml-class installation by running below commands python3 test-scikit.py
You should see the output "Scikit is installed!"
python3 test-keras.py
You should see the output "Using TensorFlow backend. Keras is installed!"