Deep learning techniques receive much of the attention lately but classical ML techniques like GBTs are sometimes faster and easier to implement. Here we'll take a look at training a model with GBTs and then we'll deploy that model to an endpoint!
By
Nick Ball
In the second part of a series on audio analysis and processing, we'll look at notes, harmonics, octaves, chroma representation, onset detection methods, beat, tempo, tempograms, spectrogram decomposition, and more!
In this overview of MLOps, Paperspace contributor Joydip Kanijilal covers the basics of MLOps and building resilient machine learning applications.
In the first part of this new series we'll explore basics of audio analysis and signal processing and we'll learn to apply basic machine learning techniques to audio.
In this post, we will cover a novel form of channel attention called the Style Recalibration Module (SRM), an extension of the popular TPAMI paper: Squeeze-and-Excitation Networks.
