31–40 of 72 posts
What would be a good book and project to get started with this? Object recognition? Product recommendations?
What would be a good book and project to get started with this? Object recognition? Product recommendations?
There are several good books written or in development, but if I had to pick one right now, I'd point you to this one: https://www.manning.com/books/deep-learning-with-pytorch
Earlier quoted context omitted.
i picked up c++ for some gpu stuff with the arrayfire api... felt the same. firstly, modern c++ takes no time to learn if you come from java / c/ c# / etc. secondly, things like operator overloading and type inference make for pretty seamless apis. E.g. want to add matrices? auto C = A + B. A lot of things suck (closures, generator functions, first order functions all suck in c++), but oh my does it all run fast when…
I'm not sure how I feel about operator overloading. It starts out simple enough, but can lead to extremely confusing code. Take this snippet of an API call[1] that parses ISO strings to chrono::time_point using date[2]: std::istringstream in(iso_string); in >> date::parse("%FT%TZ", tp); At first glace my brain cannot comprehend the second statement. Why is there an input stream going into a function ? Is that even va…
The function is constructing a temporary object and reading into that object. The object includes a reference to a named tp object, and the temporary parser object is parsing from the input stream, which writes the data into the tp object.
> but... I still don't understand all of the technical reasons behind that decision.
Because he wants to support input from an istream, and inputting from a istream necessarily means overloading '>>'. That's just how things are done in C++; if you want to support reading from or writing to an istream/ostream, you overload the '>>' and 'It's a trade off; he could have designed it so that the constructor of tp simply accepts the format string and the istream as parameters. But that has the downside of not looking like idiomatic istream code.
I personally think that the way iostreams are implemented in C++ was a mistake. (because I don't like the way idiomatic istream looks, and I think friend functions are generally smelly.) Unfortunately, it's something like 35 years too late to correct it. (I'm aware that C++ is only 33 years old; iostream.h and cout > foo; preexist C++) But here's the thing: it's badly designed iostreams that make the linked code confusing, not operator overloading.
What surprised me most is the elegance of C++ API. Compared to its equivalence in Python, the C++ version is almost the same if we discard the "auto" keyword [0]. As mentioned in the doc, they put user-friendliness over micro-optimizations, which also proves the expressiveness of modern C++ (at least when they want to prioritize user-friendliness!) [0]: https://pytorch.org/cppdocs/frontend.html#end-to-end-example
Wow, this is cool. How complete is the API? Could you use it for research? Last I checked, the TensorFlow C++ API was missing all sorts of important stuff for building models and was basically only useful for loading models saved from Python.
[disclosure: I work at FAIR]
What surprised me most is the elegance of C++ API. Compared to its equivalence in Python, the C++ version is almost the same if we discard the "auto" keyword [0]. As mentioned in the doc, they put user-friendliness over micro-optimizations, which also proves the expressiveness of modern C++ (at least when they want to prioritize user-friendliness!) [0]: https://pytorch.org/cppdocs/frontend.html#end-to-end-example
What surprised me most is the elegance of C++ API. Compared to its equivalence in Python, the C++ version is almost the same if we discard the "auto" keyword [0]. As mentioned in the doc, they put user-friendliness over micro-optimizations, which also proves the expressiveness of modern C++ (at least when they want to prioritize user-friendliness!) [0]: https://pytorch.org/cppdocs/frontend.html#end-to-end-example
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ML noob hobbyist here. Would you use PyTorch for models not involving deep neural networks too or is it just good for that. Say if I use linear models (like least squares etc) or use custom algorithms (integer linear programming, optimization, or something else...) but need very fast linear algebra support is PyTorch a good lib? I'm a C kinda guy so I usually use blas, lapack etc or numpy+pandas+sklearn in python. Wo…
PyTorch uses CuBLAS [1] under the hood, among other libraries, so basic linear algebra ops should be fast. You might also look at CuPy [2], especially if you like NumPy. [1] https://developer.nvidia.com/cublas [2] https://cupy.chainer.org/
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PyTorch uses CuBLAS [1] under the hood, among other libraries, so basic linear algebra ops should be fast. You might also look at CuPy [2], especially if you like NumPy. [1] https://developer.nvidia.com/cublas [2] https://cupy.chainer.org/
Now I'm wondering if anybody wrote a DL library on top of CuPy, and if such library could be competitive with PyTorch in terms of performance.
What surprised me most is the elegance of C++ API. Compared to its equivalence in Python, the C++ version is almost the same if we discard the "auto" keyword [0]. As mentioned in the doc, they put user-friendliness over micro-optimizations, which also proves the expressiveness of modern C++ (at least when they want to prioritize user-friendliness!) [0]: https://pytorch.org/cppdocs/frontend.html#end-to-end-example
i picked up c++ for some gpu stuff with the arrayfire api... felt the same. firstly, modern c++ takes no time to learn if you come from java / c/ c# / etc. secondly, things like operator overloading and type inference make for pretty seamless apis. E.g. want to add matrices? auto C = A + B. A lot of things suck (closures, generator functions, first order functions all suck in c++), but oh my does it all run fast when…
Earlier quoted context omitted.
i picked up c++ for some gpu stuff with the arrayfire api... felt the same. firstly, modern c++ takes no time to learn if you come from java / c/ c# / etc. secondly, things like operator overloading and type inference make for pretty seamless apis. E.g. want to add matrices? auto C = A + B. A lot of things suck (closures, generator functions, first order functions all suck in c++), but oh my does it all run fast when…
I'm not sure how I feel about operator overloading. It starts out simple enough, but can lead to extremely confusing code. Take this snippet of an API call[1] that parses ISO strings to chrono::time_point using date[2]: std::istringstream in(iso_string); in >> date::parse("%FT%TZ", tp); At first glace my brain cannot comprehend the second statement. Why is there an input stream going into a function ? Is that even va…
a.) It was not a const overload. b.) Inside said overload, it modified FOUR member variables. c.) The 'new' keyword was used twice inside said == overload. d.) I learned when gdb fails, to grep the codebase for the 'operator' keyword.
I am not a fan of operator overloading. If the SW enginner in question had instead written a equals( rhs) function, I'd have saved myself a lot of headache.