Although some claim you must start with advanced math to use deep learning, the best way for any coder to get started is with code. Rachel Thomas explains how fast.ai’s Practical Deep Learning for Coders course uses Jupyter notebooks to provide an environment that encourages students to learn deep learning through experimentation. Fast.ai wanted to help students get results fast (with no math prerequisites), so it taught them in a code-centric, application-focused way. These students are now using deep learning to identify chainsaw noise in endangered rain forests, create translation resources for Pakistani languages, reduce farmer suicides in India, diagnose breast cancer, and more. Rachel shares lessons, tips, and best practices for learning deep learning effectively so that you can set out on your own learning journey in a Jupyter notebook.
Rachel Thomas is the cofounder of fast.ai and a researcher in residence at USF Data Institute, where she teaches numerical linear algebra. Rachel helped create the free Practical Deep Learning for Coders MOOC, which 50,000 students have started. Previously, she worked as a quant in energy trading, a data scientist and engineer at Uber, and a senior instructor at Hackbright. Rachel is a popular writer on data science and diversity in tech. Her writing has made the front page of Hacker News and Medium, has been included in newsletters by O’Reilly, Fortune, crunchbase, and Mattermark, and has been translated into Spanish, Portuguese, and Chinese. Rachel holds a PhD in mathematics from Duke.
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