#timeseries #dsp #fourier #fft #psd
"After we have transformed a signal to the frequency-domain, we can extract features from each of these transformed signals and use these features as input in standard classifiers like Random Forest, Logistic Regression, Gradient Boosting or Support Vector Machines.
Which features can we extract from these transformations? A good first step is the value of the frequencies at which oscillations occur and the corresponding amplitudes. In other words; the x and y-position of the peaks in the frequency spectrum."
https://ataspinar.com/2018/04/04/machine-learning-with-signal-processing-techniques/
"After we have transformed a signal to the frequency-domain, we can extract features from each of these transformed signals and use these features as input in standard classifiers like Random Forest, Logistic Regression, Gradient Boosting or Support Vector Machines.
Which features can we extract from these transformations? A good first step is the value of the frequencies at which oscillations occur and the corresponding amplitudes. In other words; the x and y-position of the peaks in the frequency spectrum."
https://ataspinar.com/2018/04/04/machine-learning-with-signal-processing-techniques/
#dsp #pylops
https://medium.com/@matteoravasi/python-is-slow-solving-large-scale-inverse-problem-with-mpi-accelerated-multi-gpus-2dafc66b1db2
https://medium.com/@matteoravasi/python-is-slow-solving-large-scale-inverse-problem-with-mpi-accelerated-multi-gpus-2dafc66b1db2
Medium
Python is slow? Solving large-scale inverse problem with MPI-accelerated multi-GPUs
Boost your inverse problems with PyLops and its latest MPI-accelerated multi-GPUs features