Algorithmic runtime complexity improvement by the recurrent neural network
$30-250 USD
Paid on delivery
An encode-decoder enabled RNN that can generate improved runtime code. The RNN will be trained on number of coding samples from [login to view URL] and [login to view URL] and then later will be tested as well.
The project can be divided into three parts. [login to view URL] collection: Computer programs that can be solved both in polynomial time (e.g.O(Nk)) and linear time (e.g. O(N)) can be collected from the coding sites [login to view URL] or . These program files can be stored intodifferent folders where the input folder stores the polynomial time coding sample and theoutput folder will store the corresponding linear time coding [login to view URL] model training: The RNN model can be trained on the 80% of the stored [login to view URL] RNN model with encoder-decoder ([login to view URL]) or seq2seq([login to view URL] ) feature needs to be applied for better [login to view URL]: The above two steps can be tested, integrated for final analysis.
Project ID: #29973260
About the project
2 freelancers are bidding on average $245 for this job
Hi, I hope you are doing fine. I have almost 10 years of experience in machine learning algorithms. I can implement various types of artificial intelligence algorithms including yours with Matlab, Python and etc. I hav More
Hello, How are you? Thank you for watching my bid. I have many experiences with ML Learning. I have implemented robot arm control project using reinforcement learning(opencv, python, tensorflow, anaconda, dlib and so More
