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I would like to know if it is possible to read several lines from a file in parallel with my GPU in Keras. Imagine that I have a file with 50 features (i.e 50 columns) and 10k lines that I would like to process in parallel, what's the most efficient way to do it with Keras? Moreover, I don't want to train my model, since I'm already in the deployment phase of the project.
Read the entire file, create a batch with shape (10k, 50) and give it to the model:
result = model.predict(inputData)
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For a course term project we have to build a machine learning algorithm in which user fills the form and the algorithm analyse the best suitable university based on the responses. I am new in the field of machine learning and I do not know what kind of algorithm can we use. Is the recommendation systems a right approach for this?
I did some review on the internet for some similar projects, however still can not find a good resource.
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I am looking for the machine learning correct approach for predicting the lottery numbers, not the most accurate answer but at least we have some predicted output. I am implementing the regression based and neural network models for this. Is their any specific approach which follows this?
It is impossible. The lottery numbers are random - actually to be more specific, the system is chaotic. You would require the initial configuration (positions etc) to insane (possibly infinite) precision to be able to make any predictions. Basically, don't even try it.
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i am stuck with this problem. I have generated handwritten digit using GANs. And now I want to evaluate how accurate my generated handwritten digits are. Is there any way??
The most common ways to evaluate the network is to either show the visual output as in the original gan paper (http://papers.nips.cc/paper/5423-generative-adversarial-nets) or by showing on how well they do for semi-supervised learning (http://papers.nips.cc/paper/6125-improved-techniques-for-training-gans.pdf). This means using GANs to improve classification performance for the case, where only little data is available .
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Hi i want to predict health level(High,medium,low) in leaf using image processing and data mining.So far i thought using extract colors from leaf using Bayes algorithm to predict healthy of leaf. and data mining part have completed now.but i need extra features for prediction.we only used orchid leaf.So i can't use vain structure.Can anyone help me to what are the other features can be extracted from leaf for identify health level of leaf.Any idea or comments help me to improve my project. Thanks
There are many possible approaches to a problem like this. One common method is the bag-of-features model. Take a look at this example using the Computer Vision System Toolbox in MATLAB.
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I am searching for some datasets in form of 0 and 1 . i cant find dataset. i have find some 10 to 12 records but want at least 100 records and 8 different records.
This is one i have searched this link but this is very low data http://searchbusinessanalytics.techtarget.com/feature/Simple-data-mining-examples-and-datasets
Check out public data sets from Google. Maybe they'll have what you want.
Have a look at the UCI machine learning repository. There you can filter how many attributes/features or prediction classes you want and choose a dataset.
The Credit Approval dataset might suit your search criteria:
http://archive.ics.uci.edu/ml/datasets/Credit+Approval
Also there is a dataset called Mushroom dataset which should also fit to what you are looking for.