Why is Python so fashionable in machine learning? - page 3
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Uh-huh, we'll take it. MS recently released a deep-learning toolkit in the public domain. For some reason it's on the pluses )) As they write, the goal was to provide maximum speed of speech and image recognition.
https://github.com/Microsoft/CNTK
So R is also on the pluses. all the computationally intensive code uses other people's libraries. For example, the matrix operations are intel's library.
That's not how I feel about all this. The main thing is to get into the mainstream. If you start making choices, you get stuck in the selection stage. And if you sit in the mainstream, you probably don't understand what's wrong. After the inclusion of R in Microsoft, there are a huge number of features in R that I can't even appreciate, let alone use.
So, if trading, then R. For the particularly advanced, R + python. I have seen a few tips like that.
Using R, as a result you have a huge number of tools, far beyond the physical capabilities of a single person. Other than that it is a well systematized literature of all sorts. Every R function necessarily has a link to the author of the algorithm. All of this can be used as a textbook, without Googling.
So R is also on the pluses. all computationally intensive code uses alien libraries. For example, matrix operations are Intel's library.
That's not how I feel about all this. The main thing is to get into the mainstream. If you start making choices, you get stuck in the selection stage. And if you sit in the mainstream, you probably don't understand what's wrong. After the inclusion of R in Microsoft, there are a huge number of features in R that I can't even appreciate, let alone use.
So, if trading, then R. For the particularly advanced, R + python. I have seen a few tips like that.
Using R, as a result you have a huge number of tools, far beyond the physical capabilities of a single person. Other than that it is a well systematized literature of all sorts. Every R function necessarily has a link to the author of the algorithm. All this can be used as a textbook, without any googling.
I once tried to study R, tried to find implementations of digital filters and wavelets. Maybe I'm not good at searching, but the R repository is some kind of unsystematic mess, a mishmash of code. There is no partitioning, you just search by name. In short, as in the Internet dumpster, where everything is also in a pile.
Somewhere the author will write in detail what the library does, somewhere blah-blah, to get rid of. That's the impression I got back then. This is about a year ago.
Python is:
Note that unlike R, the integration of Python into environments like Java and Net is very real. For example there is IronPython for Net, you can code builds directly in Python and still access CLR resources.
You just don't know R, a lot of forums, perfectly supported, has a huge amount of useful literature for us both in the form of books and articles....
Don't sing us nice songs about tons of documentation and so on and so forth. There are very few books on R. Only Robert Kobakov has made a mark in this field. And a couple of other authors. The books themselves are very specific and very hard to understand R from them.
I don't know anything similar in Python.
Why pay attention to google. Take Microsoft. As of today R is part of Microsoft's software.
Oh, come on. Where is that part buried? Here I open VS select IronPython in nuget and in five minutes I can code in Net on it. And where do I download R for Studio?
We do not need to sing beautiful songs about the mass of documentation, etc., etc. There are very few books on R.
I have hundreds of books in my computer, from R tutorials to various sections of statistics that are implemented in R.
R is a huge statistics library and any function in R packages contains a reference to an algorithm. It's almost always open literature.
Many times I've given a link, there are hundreds of R-related books here for very limited money.
Nowadays R is the algorithmic standard for statistics and machine learning in particular.
I read the articlehttps://habrahabr.ru/post/350042/, cool machine and again Google offers all the API and development tools in Pyton. But why, it's slow, what's the point of cool hardware if you use a slow language?
Yes, I know the libraries are written in pluses and they're fast. But the user code is still in python. I dabbled with python a long time ago, maybe something extraordinary happened over the years to make it so popular?
If anybody knows anything, please write it down.
because Python^
1. lots of libraries dedicated to it
2. fast layout of information for visualization
3. language isn't tied to the OS
I think it's more convenient to declare types in advance, like in C++, instead of adding crutches, like in Python (for complex projects)
I once tried to study R, trying to find implementations of digital filters and wavelets. Maybe I don't know how to search, but the R repository is some kind of unsystematic mess, a mishmash of code. There is no partitioning, you just search by name. In short, as in the Internet dumpster, where everything is also in a pile.
Somewhere the author will write in detail what the library does, somewhere blah blah, to get rid of. That's the impression I got back then. This is about a year ago.
Here's a rubric by R
Here's a selection on time series
Here's a link to machine learning
Here 's the R in Microsoft.
Here 's the Russian version of it.
Here are the questions.
There are several packages on wavelets, like wavelets. When you open them up, there are links, and you can usually find books on how to apply wavelets to trading.
I had a whole collection, I can't find it at once, if I come across it, I'll post it.
I'll send it to you. R is mainstream and if you fail to find something, ask, my knowledge of R is very limited, but obviously more than yours, I will help you.
Java (Scala) is the standard for distributed machine learning (Spark, MXNet, Hadoop).
R and Python have only linking modules to use these systems, not full-fledged support.
Wake up. The first link in Yandex:https://tproger.ru/books/free-python-books/ There is a lot of literature (and it is fundamental and of high quality). Take for example Mark Lutz's "Learning Python".
Oh, come on. Where is this part buried? Here I open VS select IronPython in nuget and in five minutes I can code in Net on it. And where do I download R for Studio?
Got IronPython, only it doesn't install through nuget, it installs from the installer. Menu-Means-Get Tools and Components and run the installer separately. But this is the little things.
I will try to remember python...