I am new to data science I need to create code to find speedup compared with the number of processes while using a k-nearest neighbor. which (k=1,2,3,4,5,6,7). this process should be after downloading some datasets. it is preferred to use python. What is the appropriate code in python for that?
I'm trying to implement RMSProp in my own Neural Network library so I can undertand the 'under-the-hood' operations, but this specific implementation is not working / converging, and I can't figure out why. I'm pretty sure I followed the formula (RMSProp + Momentum), and here is my code : //Step 1 - Compute a hidden neuron (this == ClNeuron) error gradient (TanH) double tmpBuffer = 0.00; for (std::size_t i = 0; i<this->m_output_connections.size(); i++) { ClNeuron* target_neuron = (ClNeuron*)m_output_connections[i]->m_target_neuron; tmpBuffer += …
I was planning on making my own neural network library in C++ and was going through other's code to make sure I am on right track. Below is a sample code that I am trying to learn from. Everything in that code made sense, except for the gradient descend part, in which they literally update the weights by adding a positive learning rate. Shouldn't we take the negative of the gradient to reach the optimum? Line number: 137 - 157. …
Pytorch seems to run 10 times slower on a 16 core machine vs 8 core machine. Any thoughts on why that is and what/if any thing I can do to speed up the 16 core machine? Thank you Below is a list of details in the order in which you find them. 16 core pytorch env 16 core lscpu 8 core pytroch evn 8 core lscpu 16 core CMake Cache can be made avaible 8 core CMake Cache can be …
I use python in my day to day work as a research scientist and I am interested in learning C. When would a situation arise where python would prove insufficient to manipulate data?
I'm pretty sure this is the right forum for this, or let me know otherwise, I'll happily move this to a better place. I have a strange problem. I've written an algorithm designed to take three files of UNIX timestamps, and produce a list of triplets in order of closeness. Each triplet is unique (no two triplets share an element), each triplet has one element from each file, and each triplet {x,y,z} is created so as to minimize max(x,y,z) - …
Recently I deployed a program using libtorch (PyTorch C++ API). The program run as expected but its gives me a warning. Warning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). How do I disable the warning ?