71–80 of 122 posts
Is C/C++ still worth learning if o want to create some models from scratch (new layers or different paradigms) I hear that C++ is a nightmare to work with and was wondering if Rust,Julia, or even Swift would be worth learning instead. I know Python but deep learning frameworks seem to be written in C++, so to come up with new layers I need to understand C++, which I was told has lot of peculiarities that takes time t…
Is C/C++ still worth learning if o want to create some models from scratch (new layers or different paradigms) I hear that C++ is a nightmare to work with and was wondering if Rust,Julia, or even Swift would be worth learning instead. I know Python but deep learning frameworks seem to be written in C++, so to come up with new layers I need to understand C++, which I was told has lot of peculiarities that takes time t…
Is C/C++ still worth learning if o want to create some models from scratch (new layers or different paradigms) I hear that C++ is a nightmare to work with and was wondering if Rust,Julia, or even Swift would be worth learning instead. I know Python but deep learning frameworks seem to be written in C++, so to come up with new layers I need to understand C++, which I was told has lot of peculiarities that takes time t…
Julia is a blast to do research on this stuff in, if you want to go beyond the basics like TensorFlow and PyTorch allows. The 2020's is going to be the decade of mixing numerical PDEs with machine learning IMO, and Julia already has a lot of features along these lines that are missing from "traditional ML" libraries.
Study calculus, from the definition of real numbers and to taking complex integrals via residuals; then study linear algebra to some theorems about eigenvectors. 1 month total, assuming you're somewhat talented and determined to spend 12 hours a day learning proofs of boring theorems. After that you'll realise that most of the ML papers out there are just ad-hoc composed matrix multiplications with some formulas used…
What are complex integrals used for in machine learning?
Is C/C++ still worth learning if o want to create some models from scratch (new layers or different paradigms) I hear that C++ is a nightmare to work with and was wondering if Rust,Julia, or even Swift would be worth learning instead. I know Python but deep learning frameworks seem to be written in C++, so to come up with new layers I need to understand C++, which I was told has lot of peculiarities that takes time t…
C++ is not as tricky as people make it out to be. There is a lot of elitism among programmers, and a lot of people seem to claim it’s hard solely to make themselves look smarter for being able to write it. If you know the basics of programming and have the persistence to. RTFM (Read The Fucking Manual), C++ will not give you any trouble. In fact, you might actually start to enjoy it more than the other languages you…
Is C/C++ still worth learning if o want to create some models from scratch (new layers or different paradigms) I hear that C++ is a nightmare to work with and was wondering if Rust,Julia, or even Swift would be worth learning instead. I know Python but deep learning frameworks seem to be written in C++, so to come up with new layers I need to understand C++, which I was told has lot of peculiarities that takes time t…
No point unless you have an interest in numerical linear algebra. The people who write the foundational Fortran/C/C++ libraries are experts in numerical analysis which is another rabbit hole. If you want to write your own for fun, then there are some great algebra libraries in C++ you can use or you can use bindings for PyTorch or TF.
I was originally trying to create a new type of convolution layer in Keras and asked in their official google board, stackoverflow etc , after being stuck for a while but the answers I got weren’t solving the problem.
I haven’t tried creating custom layers in Pytorch yet though so maybe it’s possible to do so with Pytorch and can just learn C++ for other purposes.
Is C/C++ still worth learning if o want to create some models from scratch (new layers or different paradigms) I hear that C++ is a nightmare to work with and was wondering if Rust,Julia, or even Swift would be worth learning instead. I know Python but deep learning frameworks seem to be written in C++, so to come up with new layers I need to understand C++, which I was told has lot of peculiarities that takes time t…
You can actually implement most new layers or experimental ideas using frameworks like pytorch or tensorflow. They support fairly low-level primitives which are much more flexible than keras or pytorch sequential models. That said C/C++ is still very useful for implementing high performance systems.
Earlier quoted context omitted.
You can actually implement most new layers or experimental ideas using frameworks like pytorch or tensorflow. They support fairly low-level primitives which are much more flexible than keras or pytorch sequential models. That said C/C++ is still very useful for implementing high performance systems.
Ah. I haven’t played around with Pytorch custom layers enough so I am going to give it a try. I was initially trying to do it in keras but Keras was just using tensorflow layers for most operations so I couldn’t tweak the original tensorflow layers through keras easily.
I'm impressed by the responses generated in this conversation. My expectation was to get several links and start browsing each one of them. However, many have agreed that the best way is to start with a specific example and start creating a model. Many times I have tried to answer that same question, "which model to apply"? How do I know I'm not re-inventing the wheel?
Earlier quoted context omitted.
I definitely agree that you don't need to go deep into theory to be able to do useful things. But I think the bias-variance tradeoff is a very bad example of "useless theory". It's essentially just another name for overfitting/underfitting, which are approximately the most important ML concepts there are.
I would again argue, the natural progression for this concept would be: 1.) Trains classifier 2.) My train error was so low! Why is my validation error so high 2.) Googles -> Why is my classifier training error lower than my validation error 3.) Learns about overfitting 4.) learns about bias variance Its always a natural progression. Reading about this stuff without encountering it means it usually doesnt stick, and…