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1

I see multiple reasons to take a different route: While in "classical" pattern recognition you might have done things like feature engineering outside of your model, one idea of deep learning was to "insource" it into the model and let the model take care of "everything". Following that, I have seen a general tendency in deep net architectures to let your ...


1

Hello and welcome to the community. There are multiple ways you can train a neural network: stochastic, mini-batch and batch. What you explained is the stochastic mode, where you input one training example 01 for example, calculate the gradients and update the networks weights before the next training example is fed. You could also select multiple such ...


2

I think you're looking for the minimization of false positives, that is, the instances that are classified as belonging to the desired class (the positive part of false positives) but that do not actually belong to that class (the false part of false positives). In practice, given your constraints, you may want to maximize the precision, while maintaining a ...


2

Of course. It only depends if those features are informative enough for the task at hand. In order to better understand the phenomenon, you can imagine 2 features displayed as points in a 2D plane. The number of possible target classes goes up to the number of clusters you can find in that plane. About the suggestion, I can only recommend the utilisation of ...


1

there's a lot to un-pack in this question. Why do they only pick 500 rows? my guess: in order to keep the example running quickly. tsfresh usually takes a while to calculate its features. note that when they evaluated their model, they took the last 500 samples. What's the point of re-arranging the rows/columns? answer: the data frame format that ...


2

I try to answer the things I know for sure: One effect of bigger images is the increasing computation time due to more pixels (input to your training) 4.Grayscaling reduces the information, which might decrease training time, but also model performance (accuracy, precision, recall). What I have seen is that grayscaling is used in for example face detection ...


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If the task involves only apples, orange and peaches, you should use method 1. As the number of classes is small, the network cannot generalize well to all classes. As a side note, you should start with the pretrained weights of YOLO v3 as some classes of YOLO v3 may be fruits, which can help your model converge faster. If the number of classes is large, ...


1

I think you may have a class imbalance problem here, if I am reading your output correctly. You have 20,000 negative examples, but only 8000 positive ones, and you are minimizing binary cross entropy without re-weighting the examples, so your model can achieve a low-ish loss just by consistently outputing a value close to 0. This forms a local optima in the ...


3

When someone is able to do a causative attack it means there is a mechanism by which they are able to input data into the network. Maybe a website where people can input their images and it outputs a guess on what is in the picture and then you click if it got it right or not. If you continue to input images and lie to it it will obviously get worse and ...


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