A lot of people assume that you need all kinds of hard-to-find stuff to get great results with deep learning, but as you’ll see in this course, those people are wrong. The only prerequisite is that you know how to code (a year of experience is enough), preferably in Python, and that you have at least followed a high school math course.ĭeep learning is a computer technique to extract and transform data–-with use cases ranging from human speech recognition to animal imagery classification–-by using multiple layers of neural networks. It doesn’t matter if you don’t come from a technical or a mathematical background (though it’s okay if you do too!) we wrote this course to make deep learning accessible to as many people as possible.

He went on to achieve first place in the prestigious international RA2-DREAM Challenge competition! He developed a multistage deep learning method for scoring radiographic hand and foot joint damage in rheumatoid arthritis, taking advantage of the fastai library. For instance, Isaac Dimitrovsky told us that he had “ been playing around with ML for a couple of years without really grokking it… went through the fast.ai part 1 course late last year, and it clicked for me”. Many students have told us about how they’ve become multiple gold medal winners of international machine learning competitions, received offers from top companies, and having research papers published. Previous fast.ai courses have been studied by hundreds of thousands of students, from all walks of life, from all parts of the world. Start watching lesson 1 now! Is this course for me?

Practical deep learning for coders