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I am new to machine learning and linear separability.
Let's say curve1 is sin(x) and curve2 is sin(x) + 0.5
What non linear transformations or any other method should be implemented on these curves to make them linearly separable?
The curves are separable if we increase the constant part in the second curve to more than 2.
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Is it possible to use LIDAR or RGB-D camera to scan the melting pool in laser welding process?
I want to know if a LIDAR or RGBD cameras work correctly in hot and high lighting intensity like laser welding.
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I am new to music genre recognition and I am trying to do a project which classifies which genre a certain music clip is(I am using GTZAN).
I've came across an open source code in kaggle and I have used his preprocessing
(link)
I saw them using MFCC, but I need to understand why they are using these values(For example why 13 coefs?)
Moreover, I need to understand what is MFCC, I have basic knowledge of physics but it is very difficult to understand what it represents, and why he chose these values(I don't need to understand broad physics behind it, just as simple as possible please).
Another question,
MFCC image example
for example, the X axis here represents the time, but what the squares, or the Coefs represents in Y axis?
I have tried to search in the internet, but there are a lot of physics and music theory behind it, I need a simple explanation.
Thanks.
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Supervised learning is divided into two processes: learning and prediction, which are completed by learning system and prediction system. In the learning process, the learning system uses the given training data set to obtain a model through learning or training, which is expressed as conditional probability distribution or decision function
I just want to know why to add " ^ " on the top of "f(x)" or "P(Y|X)"
In statistics, that symbol is used to denote an "estimator" of a random variable.
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In many examples of Binary Classification with Deep learning
Why are linear layers used? I've been trying to look around the internet for information on the reason for the use of linear layers
e.g.
https://github.com/StatsGary/PyTorch_Tutorials/blob/main/01_MLP_Thyroid_Classifier/PyTorch_Binary_From_Scratch.py
https://hutsons-hacks.info/building-a-pytorch-binary-classification-multi-layer-perceptron-from-the-ground-up
Linear layer is just another (a bit mathematically incorrect) name of a fully connected layer, the most standard, classic, and in some sense - powerful building block of neural networks. Networks built purely from fully connected layers are universal approximators, and thus a good starting point for any sort of investigation.
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I am using support vector regressor. I want to predict personality as shown in screenshot! Is it possible to predict when y is in string format? I used onehot encoder but its not working.
This is not a regression task, but classification. 'not working' is not very informative, however normally you'd just map classes to integers. Either sklearn.preprocessing.LabelEncoder, sklearn.preprocessing.label_binarize().argmax(axis=1), pandas.factorize() or manual mapping should get the job done.
Worth noting support vector machines don't handle multiclass problems natively, so you may encounter troubles depending on the exact model you use. At least the latest sklearn versions should handle it automatically when using models like sklearn.svm.LinearSVC, building N binary classifiers under the hood.
I'd also recommend getting acquainted with a more elegant way of ensembling SVMs for multiclass problems, using sklearn.multiclass.OutputCodeClassifier().