his article was originally adapted from a podcast, which you can check out here.
For last week’s Five-Minute Friday episode, I provided a summary of the various methods of undertaking my deep learning curriculum, be it via YouTube, my book, or the associated repository of GitHub code. I mentioned at the end of the episode that while teaching this deep learning content to students online and in-person, I discovered that many folks could use a primer on the foundational subjects that underlie machine learning in general and deep learning in particular. So after publishing all my deep learning content, I set to work on creating content that covers these subjects that are critical to understanding machine learning expertly — namely, those subjects are linear algebra, calculus, probability, statistics, and computer science.
Way back in Episode #474 of this podcast, I detailed why these particular subject areas form the sturdy foundations of what I call the Machine Learning House . As a quick recap, the idea is that to be an outstanding data scientist or ML engineer, it doesn't suffice to only know how to use machine learning algorithms via the abstract interfaces that the most popular libraries (e.g., scikit-learn, Keras) provide. To train innovative models or deploy them to run performantly in production, an in-depth appreciation of machine learning theory may be helpful — or even essential. To cultivate such an in-depth appreciation of ML, one must possess a working understanding of the foundational subjects, which again are linear algebra, calculus, probability, stats, and computer science:
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