I myself did some of Andrew Ng’s courses, and I understand where you’re coming from. Although this was several years ago, but I do remember Octave!
I saw your guide: https://github.com/Simon-Holloway/Full_Math_CS_Guide I just want to say: Real Analysis is overkill in my opinion if your goal is to simply become an AI researcher. Also, I personally like Karpathy’s advice (which seems like it should radically alter your guide):
How to become expert at thing: 1 iteratively take on concrete projects and accomplish them depth wise, learning “on demand” (ie don’t learn bottom up breadth wise) 2 teach/summarize everything you learn in your own words 3 only compare yourself to younger you, never to others
Also, just as a side note, even mathematicians do this in research if not for basic things like real analysis:
Most big number theory results are apparently 50-100 page papers where deeply understanding them is ~as hard as a semester-long course. Because of this, ~nobody has time to understand all the results they use—instead they “black-box” many of them without deeply understanding. ― https://twitter.com/benskuhn/status/1419281155074019330
I agree that nothing bets practical projects, but in modern life, you need to learn a lot of background information before jumping into the real world. There are plenty of ML projects and examples that are equivalent to the ToDo (12-factor app) in complexity—single component, boundaries clearly defined. The next steps in the real world would be—here is a payment platform with 270+ services and components, how does your AI/ML component fit into it? Who do you talk to to figure out the business value of the AI/ML component (business analysis/domain driven design)? How do you talk to your creative colleagues who are responsible for user experience in a productive manner ( i.e. jobs to be done )?
I see this gap quite consistently and I am trying to address it on the technical side by building medium size AI/ML project with 3 pipelines http://thepattern.digital/ and I think modern ML/AL professionals need to know things above before jumping into any real-world project.
I myself did some of Andrew Ng’s courses, and I understand where you’re coming from. Although this was several years ago, but I do remember Octave!
I saw your guide: https://github.com/Simon-Holloway/Full_Math_CS_Guide I just want to say: Real Analysis is overkill in my opinion if your goal is to simply become an AI researcher. Also, I personally like Karpathy’s advice (which seems like it should radically alter your guide):
How to become expert at thing:
1 iteratively take on concrete projects and accomplish them depth wise, learning “on demand” (ie don’t learn bottom up breadth wise)
2 teach/summarize everything you learn in your own words
3 only compare yourself to younger you, never to others
― https://twitter.com/karpathy/status/1325154823856033793?s=20
Also, just as a side note, even mathematicians do this in research if not for basic things like real analysis:
Most big number theory results are apparently 50-100 page papers where deeply understanding them is ~as hard as a semester-long course. Because of this, ~nobody has time to understand all the results they use—instead they “black-box” many of them without deeply understanding.
― https://twitter.com/benskuhn/status/1419281155074019330
I agree that nothing bets practical projects, but in modern life, you need to learn a lot of background information before jumping into the real world. There are plenty of ML projects and examples that are equivalent to the ToDo (12-factor app) in complexity—single component, boundaries clearly defined. The next steps in the real world would be—here is a payment platform with 270+ services and components, how does your AI/ML component fit into it? Who do you talk to to figure out the business value of the AI/ML component (business analysis/domain driven design)? How do you talk to your creative colleagues who are responsible for user experience in a productive manner ( i.e. jobs to be done )?
I see this gap quite consistently and I am trying to address it on the technical side by building medium size AI/ML project with 3 pipelines http://thepattern.digital/ and I think modern ML/AL professionals need to know things above before jumping into any real-world project.