Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
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Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#2Is anyone using any of this math? My guess is no. At best it provides "moral support" for deep learning researchers who want to feel reassured that what they are attempting to do is not impossible.
Glad to be proven wrong, though.
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#3[flagged]
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#4Is it common to publish books directly to ArXiv, especially books that have just been released?
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#5Is anyone using any of this math? My guess is no. At best it provides "moral support" for deep learning researchers who want to feel reassured that what they are attempting to do is not impossible. Glad to be proven wrong, though.
Describing it as "moral support" really sells it short.
Imagine computer science without sorting algorithms, search algorithms, etc that have been proven correct and have known proven properties. This math serves the same purpose as CS theory.
So yes, if you're just fitting a model from a library like Keras, you're not really "using" the math. If you're working with data sets below a certain size, problems below a certain level of complexity, and models that have been deployed for many years and have well studied properties, you can do a lot with only a cursory understanding of the math, much like you can write perfectly functional web apps in Python or Java without really understanding how the language runtime works at a deep level.
But if you don't actually know how it works, you're going to get stuck pretty badly if you encounter a situation that isn't already baked into a library.
If you want to see what happens when you don't know the underlying math, look at the current generation of "data science" graduates, who don't know their math or statistics fundamentals. There are plenty of issues on the hiring side of course, but ultimately the reason those kids aren't getting jobs is that they don't actually know what they're doing, because they were never forced to learn this stuff.
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#6[flagged]
[deleted]
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#7Is anyone using any of this math? My guess is no. At best it provides "moral support" for deep learning researchers who want to feel reassured that what they are attempting to do is not impossible. Glad to be proven wrong, though.
According to the abstract it covers different ANN architectures, optimization algorithms, probably backpropagation.. so um yes? That is stuff anyoke in machine learning uses everyday?
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#8[flagged]
It looks to me like most of the space is taken up with a plot of the sine function and the python code to generate the plot. Maybe it's a little fluffy, but it might be good for somebody self-taught, or a young person learning all of this stuff for the first time and wanting a quick reference.
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#9Is it common to publish books directly to ArXiv, especially books that have just been released?
It's not too uncommon to see books available online from an official location. At least math and CS textbooks
Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
#10Is anyone using any of this math? My guess is no. At best it provides "moral support" for deep learning researchers who want to feel reassured that what they are attempting to do is not impossible. Glad to be proven wrong, though.
Some people like to think and communicate in dense math notation. So, yes.