In preperation for our 10 week course we want to offer up some resources for students interested in getting a head start.
We have a series of online videos that assume no knowledge of deep learning or machine learning and cover all of the basic applications. Each video is around 20 minutes. If you want to get a feel for what the class will be like or brush up on the fundamentals, feel free to watch them at your own pace: https://www.wandb.com/classes/intro/overview. We will cover a lot of these topics in more depth in person.
The only real requirement for these classes is a solid understanding of python. If you can follow along with the videos, you will be in good shape. If not, you may want to brush up on your python with a book or an online course. The books Deep Learning with Python and Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems are excellent compliments to the class materials.
All of the code for the classes and more is in the ml-class repository. Feel free to check it out and explore any topics of interest: https://github.com/lukas/ml-class
We strongly believe that the best way to learn machine learning is by doing machine learning. We have several community projects you can start with and compare your results to other students.
- Predicting frames of cats in videos link
- Predicting japanese kanji characters link
- Generating realistic images from low resolution images link
We generally do the classes in a jupyter hub environment. We've (hopefully) made setup easy. If you have any questions, please don't hesitate to reach out.
- Signup for an account with wandb
- Install docker on your laptop
- Install wandb from pip:
pip install wandb - Run
wandb loginfrom your command prompt to authenticate your machine - Clone our repository and launch a development environment:
git clone https://github.com/lukas/ml-class
cd ml-class
wandb docker --jupyter- Access the development environment at
http://localhost:8888