Join devRant
Do all the things like
++ or -- rants, post your own rants, comment on others' rants and build your customized dev avatar
Sign Up
Pipeless API
From the creators of devRant, Pipeless lets you power real-time personalized recommendations and activity feeds using a simple API
Learn More
-
I always consider building machine learning atleast as a 3 layer. front-end, dataflow, backbone execution.
front-end : For showing, interpreting, presenting the data, result, and etc. my project use JS and Ruby.
dataflow : Main programming for the Neural Network, defining the type of the NN, etc. Personally i use Python and/or Cython.
BbE : this layer is kinda vague and kinda indistinguishable from dataflow. but BbE handle the execution, threading, CUDA programming, the API, etc. I strictly use C/C++ for this and sometimes wrapped with Python -
Thank you both for the input!@wowotek @koolkobra
The website of Alphabet(parent company of Google) - https://abc.xyz, has hidden link to Hooli, the tech compan...
I was talking to a friend about the current state of machine learning through tensorflow and commented about the use of Javascript as a language.
He discarded the idea as he views Javascript as something that should only be used as a frontend technology rather than something to build backends or deep learning models.
I am thorn. I have always liked Javascript but will admit that I have used it mostly in the area of front end with very few backend instances(i did create a full stack intranet app in Express once, major success for the application it was hosting, it was a very basic api which had its own nosql db with no need to interact with the company's relational data, it was perfect for the occasion and still help maintaining it from time to time)
My boi states that node's biggest issue has always been npm and the quality of packages. I always contradict those statements by saying that if one uses community standards and the best packages then one does not need to worry about the quality(i.e mongoose over some unmaintained mongo wrapper etc)
I sometimes catch myself finding that my way of thinking adapts better to JS than it even does Python (which is his preference for deep learning) and whilst there are some beastly packages for python in terms of quality and usefulness such as matplotlib etc that one can do great things with the equivalent JS.
I mean, tensorflow.js came from the same wizards that did tensorflow (obviously) and i find the functional approach of JS to be more on par with how we develop solutions.
I am no deep learning expert, and sadly I have no professional experience with machine learning. But I venture to say that we should not cast aside the great strides that the JS community has done to the language in terms of evolution and tooling. Today's Js is not your grandaddy's Js and thinking that the language is crippled because of early iterations of the language would be severely biased.
What do you guys(maybe someone with professional experience) think of Js as a language for machine learning?
Do you think the language poses something worth considering in terms of tooling and power for ml?
question
interested
wondering