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These are specific bite-sized projects to learn an aspect of deep learning, starting from scratch. There is an associated video around 10 minutes long. I assume no background in ML but some proficiency in python. The projects are in order from beginner to more advanced, but feel free to skip around.

If you have done all of the tutorial projects, we have a few more that don't have associated lessons yet!You can learn by contributing to one of our collaborativeBenchmarks.

1. Clone this repository
2. Get the python libraries (run 'pip install -r requirements.txt')

You don't need a fancy computer to run most of the examples, but especially to do the later projects you may want to invest in a GPU.

O'Reilly 9.10.2019 - Using Keras to classify text using LSTMs

Introduction to Machine Learning

In my in-person classes, I typically use a lot of the examples in the directoryexamples. This code is liable to change as I update things.

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.

Install git if you don't have it:https://git-scm.com/download/win

Installanaconda

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=3.6activate ml-class

Whenever you start a new terminal, you will need to callactivate ml-class.

git clone https://github.com/lukas/ml-class.gitcd ml-class

pip install wandbconda install -c conda-forge scikit-learnconda install -c conda-forge tensorflowconda install -c conda-forge keras

You can download python fromhttps://www.python.org/downloads/. There are more detailed instructions for windows installation athttps://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 the 64 bit version of python 3.5 or 3.6 in order to install tensorflow.

git clone https://github.com/lukas/ml-class.gitcd ml-class

If you get an error message here, most likely you don't have git installed. Go tohttps://www.atlassian.com/git/tutorials/install-git for instructions on installing git.

pip install -r requirements.txt

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 outhttp://thepythonguru.com/.

If you're on a Mac check outhttp://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 outhttps://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 athttp://pandas.pydata.org/pandas-docs/stable/10min.html. There is a great overview of numpy athttps://docs.scipy.org/doc/numpy/user/quickstart.html.