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I'm preparing my dataset to be preprocessed before training with CNN model but i couldn't generate data from this type of file which contain several signals.
I recommend using the gdflib library. It'll allow you to process your .gdf files by organizing your data into nodes for further processing.
It would also help if you could please provide a minimal reproducible example of what you have tried.
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I want to train a model using RandomForestClassifier but there are infinite values in my data
How can I handle infinite values in the pipeline before the algorithm?
Can anyone help me?
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Assuming that I have an excel sheet with three columns of data and one column of label. Can I use all three columns of data to train a neural network to predict the label?
Definitely. Here is a sketch of such a neural network:
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Is it doable to update my trained model (fbprophet) in order to surpass the dilemma of retraining the whole dataset every time?
Thanks for your help in Advance
I found a solution using the warm-start approach, here.
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It would be really helpful if someone can explain and give an example of how to apply machine learning algorithms using scikit-learn and python to images,sound or videos. I know how to apply it to csv file just want to learn how it can be extended for multimedia files.
Thankyou
There's a section in the sklearn documentation on feature extracting that focuses on working with images. There's also a section of the docs that talk about working with images, video and audio. I suggest you spend some time going through these sections and the rest of the documentation.
The MNIST dataset is a standard dataset of images of hand written digits that is used in a lot of examples so if you're searching google for examples "MNIST sklearn" will probably be helpful.
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I am working on pattern recognition program using R/python. What would be the best way to compare two or more figures and identify/recognize the similar or duplicate figures based on pattern recognition?
There are lots of papers on the internet, we can try to get the idea how to extract and process feature in a fingerprint. For instance, http://www.cse.unr.edu/~bebis/CS790Q/PaperPresentations/MinutiaeDetection.pdf
Then you can use whatever classifier you want such as support vector machine.
If you need more idea you can visit http://dermatoglyphics.org/11-basic-patterns-of-fingerprint/ to generalize