deep-learning-with-python-notebooks

for Coders with Fastai and PyTorch: AI Applications Without a PhD" by Jeremy Howard and Sylvain Gugger. They have the lectures for several years on machine learning and deep learning. There are many books and resources on how to train models. However, there are extremely few that enable you to do ML in a non toy project setting and do useful things in the real world. One recent course is CS329s "Machine Learning Systems"[8] that fits nicely after CS229. I only skimmed some slides, but it appears to be interesting. Disclaimer: Yes many publications about deployment, and ML project lifecycle management and platforms, but I have blocked most of them, including the largest publication about data science and machine learning on Medium from search results in my browser, because it publishes garbage posts from people who seem to never have worked on a project with actual stakes. I know it was not your question, but I guess you'd like to do something with ML in the real world. There is a range of skills and process necessary to make things happen. Identifying the right problem, designing the right metrics (tricky, especially when working with a project involving "intangibles"). Reframing. These also require interviewing skills and deep dives into a subject matter (so the ability to discover and curate the right resources, then read and synthesize) to be able to interact with domain experts and see a project through. - [0]: https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599 - [1]: https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXSh... - [2]: https://www.youtube.com/playlist?list=PLD63A284B7615313A - [3]: https://work.caltech.edu/telecourse - [4]: https://www.manning.com/books/deep-learning-with-python - [5]: https://github.com/fchollet/deep-learning-with-python-notebo... - [6]: https://www.oreilly.com/library/view/data-science-from/97814... - [7]: https://github.com/joelgrus/data-science-from-scratch - [8]: https://stanford-cs329s.github.io/syllabus.html