3 ms·

Understanding the notation is only the first little step in understanding the underlying principles. There are a lot of machine learning books/classes that wil

Understanding the notation is only the first little step in understanding the underlying principles. There are a lot of machine learning books/classes that will cover the underlying principles you need to understand what's covered in the book. Exactly which one to recommend depends on your background, but Andrew Ng's Coursera Course [1] has been a staple for people without a lot of math background. [1] https://www.coursera.org/learn/machine-learning?authMode=login https://www.coursera.org/learn/machine-learning?authMode=log...

sonabinu 8y ago
I tried to overcome the notation issue by taking a ML class as a non- degree seeking student from the CS dept of a good university. I took it for credit, so invested a lot of time on hw and paper reading. I’m more comfortable with the notation now and am better at learning independently. I also did calc, linear algebra and Differential equations classes at a community college prior to this.
sqrt17 8y ago
Ng's Coursera course will teach you what happens in Deep Learning and Machine Learning, but at least the Deep Learning course is very very light on the math side and avoids scary mathematics rather than making it accessible. Without having read it, I'd recommend the "preliminary maths" part of Bengio et al's Deep Learning book - it teaches both the letters and the language, so to speak, and if the language isn't for you, you'd better throw away the papers and completely concentrate on reading and understanding the implementations that are our there, using the implementation first and foremost and the paper only as a backup to provide explanations when the implementation does mysterious or unexplained things. You can do deep learning productively without having a PhD, but you won't be able (nor obliged) to read and understand PhD-level academic papers unless you have a solid (i.e., maths or physics or math-rich CS BSc) maths background.
faceplanted 8y ago
It's the first little step but you can't discount it, I struggled through my CS degree being "bad at symbols" until I found the book in the top comment and things got significantly easier, because even the mathematical courses that don't expect much maths background are taught by people who do have that background and often fall back on those symbols and the vocabulary they've got the experience with. Honestly it's weird getting this far without knowing the symbols well, I went to a significantly poorer and less well staffed school that most of my fellow students, and you're kind of between a rock and a hard place sometimes, because no-one sees it as their job to catch you up, and the people who do know it learned it long ago manually and don't exactly have an easy time teaching you if you ask.
andyjohnson0 8y ago
+1 for Andrew Ng's ML course. I did the course earlier this year, and I can confirm that you don't need much more than high-school level maths knowledge. If you understand concepts like functions and summation then you're most of the way there, and if you've got some calculus then it should be easy. I came out of it with better mathematical comprehension than when I went in. I found that I spent a lot of time on the course converting mathematical notation into code (octave/matlab), which is a great way to validate your understanding of the maths. If you're understanding is wrong then it either throws an error, or runs slowly because you failed to map summations etc onto the appropriate parallelised operations. ML has moved on a bit since the course was designed, but it's still a good way to get familiar with the basics.

sonabinu 8y ago

I tried to overcome the notation issue by taking a ML class as a non- degree seeking student from the CS dept of a good university. I took it for credit, so invested a lot of time on hw and paper reading. I’m more comfortable with the notation now and am better at learning independently. I also did calc, linear algebra and Differential equations classes at a community college prior to this.

sqrt17 8y ago

Ng's Coursera course will teach you what happens in Deep Learning and Machine Learning, but at least the Deep Learning course is very very light on the math side and avoids scary mathematics rather than making it accessible. Without having read it, I'd recommend the "preliminary maths" part of Bengio et al's Deep Learning book - it teaches both the letters and the language, so to speak, and if the language isn't for you, you'd better throw away the papers and completely concentrate on reading and understanding the implementations that are our there, using the implementation first and foremost and the paper only as a backup to provide explanations when the implementation does mysterious or unexplained things. You can do deep learning productively without having a PhD, but you won't be able (nor obliged) to read and understand PhD-level academic papers unless you have a solid (i.e., maths or physics or math-rich CS BSc) maths background.

faceplanted 8y ago

It's the first little step but you can't discount it, I struggled through my CS degree being "bad at symbols" until I found the book in the top comment and things got significantly easier, because even the mathematical courses that don't expect much maths background are taught by people who do have that background and often fall back on those symbols and the vocabulary they've got the experience with. Honestly it's weird getting this far without knowing the symbols well, I went to a significantly poorer and less well staffed school that most of my fellow students, and you're kind of between a rock and a hard place sometimes, because no-one sees it as their job to catch you up, and the people who do know it learned it long ago manually and don't exactly have an easy time teaching you if you ask.

andyjohnson0 8y ago

+1 for Andrew Ng's ML course. I did the course earlier this year, and I can confirm that you don't need much more than high-school level maths knowledge. If you understand concepts like functions and summation then you're most of the way there, and if you've got some calculus then it should be easy. I came out of it with better mathematical comprehension than when I went in. I found that I spent a lot of time on the course converting mathematical notation into code (octave/matlab), which is a great way to validate your understanding of the maths. If you're understanding is wrong then it either throws an error, or runs slowly because you failed to map summations etc onto the appropriate parallelised operations. ML has moved on a bit since the course was designed, but it's still a good way to get familiar with the basics.