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I'm interested in applying machine learning algorithms for SAT solving procedures. Current trends on SAT solvers seems they use CDCL procedures.
Specifically, is there any small example to illustrate the idea?
The closest work I'm aware of is by Selsam et al., "Learning a SAT Solver from Single-Bit Supervision." See https://arxiv.org/abs/1802.03685
You should also look into Selsam's PhD thesis on this topic as well https://searchworks.stanford.edu/view/13250178, which has lots of other references.
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I just read this blog post about Machine Learning, Clustering and Cluster Computing.
My question is, how do I get started in Machine Learning ?
You need to have basic knowledge of Calculus, Statistics, Probability, and Linear Algebra.
After that, you can build a roadmap for your career, or maybe you can find some Machine Learning roadmaps on google.
Machine Learning by Andrew NG is also a good course to start.
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I am looking for beginner Machine Learning Linear Regression problems. I searched in Kaggle, but couldn't find a proper one. Can you please suggest me a beginner problem from Kaggle, or from any other platform?
Thanks in advance.
Kaggle has tons of linear regression notebooks and datasets to learn from, most popular ones are probably about house pricing (given certain house features predict it's price).
Here's a new one I'm looking forward to solve:
Ben & Jerry's flavours and ratings ---> products.csv
The main goal would be predict wich ice cream flavours are better accepted based on it's ingredients.
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I was curious if anyone knew of something like this flowchart but for Computer Vision tasks? Specifically for OpenCV would be most ideal.
Or any references with best practices, and common patterns for Computer Vision problems?
That's a monumental task. The best I could find is from this article and it's a little bit old:
Maybe it's a good time to commit to FlexCV on Kickstarter.com, a GUI for OpenCV that allows you to create complex algorithms in a matter of minutes by connecting graphical elements together. It's an alternative for Adaptive Vision, but purely based on OpenCV features.
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I want to know what are the appropriate tools for each step to analyse sentiment :
removing stopwords,
stemming,
Vector Representation of Text,
feature selection,
classification,
how to pass from Vector Representation of Text to feature selection , is there any step to follow and an examlpe which describe those steps
You can take a look at Bing Liu's tutorial on sentiment analysis here, and Pang & Lee's survey here. They cover the different aspects of sentiment analysis and common approaches.
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I need to fit some data in an iOS app, basically with Newton's Law of Cooling:
y = a + b * c^{x}
I found the GNU Scientific Library is likely to handle this, however, it is out of consideration for the sake of GPL. Is there any open-sourced nonlinear Chi-Square solver in C/C++ with a commercial friendly license (e.g., BSD, APL etc.)?
Looking forward to your suggestion.
It seems that MINPACK could do the job.
https://github.com/johannesgerer/jburkardt-cpp/tree/master/minpack