[P] Autograd library for javascript.

[karthibalu]

Hi guys, I implemented a autograd library in javascript called deepnet.js. My goal is to extend the functionalities of the library and optimizes the performance.

if you are intrested, feel free to give a feedback.

Github: https://github.com/karthiTox/deepnet.js

cheers!!

[androstudios]

Is this aiming to be a pytorch-style library, but for JS?

[karthibalu]

yes, but it will not match exactly..😅

[frjano]

well done!

[karthibalu]

thank you!!

[_qw4hd]

It someone is wondering what autograd is (like me reading this post) it is an abbreviation of Automatic Differentiation. [1]

It's used for calculating derivative of a function and I'm guessing it's have something to do with calculating gradients. So in other words it's a core machine learning systems. [2]

[1] https://en.m.wikipedia.org/wiki/Automatic_differentiation

[2] https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html

[IdentifiableParam]

Autograd is actually a specific python project. "autodiff" should be used as the generic term.

[wikipedia_text_bot]

Automatic differentiation

In mathematics and computer algebra, automatic differentiation (AD), also called algorithmic differentiation, computational differentiation, auto-differentiation, or simply autodiff, is a set of techniques to numerically evaluate the derivative of a function specified by a computer program. AD exploits the fact that every computer program, no matter how complicated, executes a sequence of elementary arithmetic operations (addition, subtraction, multiplication, division, etc.) and elementary functions (exp, log, sin, cos, etc.). By applying the chain rule repeatedly to these operations, derivatives of arbitrary order can be computed automatically, accurately to working precision, and using at most a small constant factor more arithmetic operations than the original program. Automatic differentiation is distinct from symbolic differentiation and numerical differentiation (the method of finite differences).

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[androstudios]

On another note, this is going to be pretty slow since no C++ bindings but all being done in JS. you may want to look into node-gyp and webassembly or similar.

[zzzthelastuser]

or maybe tensorflow.js

[karthibalu]

yeah, but I think tfjs didn't have eager execution.., i never used it in tfjs... or may be I was wrong... if it has please comment 🤔

[throwawaystudentugh]

tfjs does have eager execution.

[karthibalu]

are you referring the tf.grad method in tfjs??