fast.ai Courses

Practical Deep Learning For Coders

3.8

This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one—learning how to get a GPU server online suitable for deep learning—and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. There are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded during the first certificate course at The Data Institute at USF. Part 2 will be taught at the Data Institute from Feb 27, 2017, and will be available online around May 2017.

Introduction to Machine Learning for Coders!

4

Welcome to Introduction to Machine Learning for Coders! taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic). Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building data products.There are around 24 hours of lessons, and you should plan to spend around 8 hours a week for 12 weeks to complete the material. The course is based on lessons recorded at the University of San Francisco for the Masters of Science in Data Science program. We assume that you have at least one year of coding experience, and either remember what you learned in high school math, or are prepared to do some independent study to refresh your knowledge. 

Computational Linear Algebra

0

The primary resource for this course is the free online textbook of Jupyter Notebooks, available on Github. They are full of explanations, code samples, pictures, interesting links, and exercises for you to try. Anyone can view the notebooks online by clicking on the links in the readme Table of Contents. However, to really learn the material, you need to interactively run the code, which requires installing Anaconda on your computer (or an equivalent set up of the Python scientific libraries) and you will need to be able to clone or download the git repo.Accompanying the notebooks is a playlist of lecture videos, available on YouTube. If you are ever confused by a lecture or it goes too quickly, check out the beginning of the next video, where I review concepts from the previous lecture, often explaining things from a new perspective or with different illustrations.You can ask questions or share your thoughts and resources using the Computational Linear Algebra category on our fast.ai discussion forums.This course assumes more background than our Practical Deep Learning for Coders course. It was originally taught during the final term before graduation of the USF Master of Science in Analytics program, and all students had already participated in a “Linear Algebra Bootcamp”. If you are new to linear algebra, I recommend you watch the beautiful 3Blue 1Brown Essence of Linear Algebra video series as preparation.