How the Jupyter Notebook helped fast.ai teach deep learning to 50,000 students
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.
| Talk Title | How the Jupyter Notebook helped fast.ai teach deep learning to 50,000 students |
| Speakers | Rachel Thomas (fast.ai) |
| Conference | JupyterCon in New York 2017 |
| Conf Tag | |
| Location | New York, New York |
| Date | August 23-25, 2017 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
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.
