Shubham

Density Estimation, Dot Products, Convolutions and everything…Source: Image by authorKernels or kernel functions are beautiful mathematical concepts that are used in machine learning and statistics with different forms. If you’re a beginner you might feel tempted to know the exact definition of kernels, but you may get confused by multiple definitions of kernels that are explained across blogs/websites on the internet.The kernel function is a confusing concept, just because the knowledge around it is decentralized across…

Covariance, eigenvalues, variance and everythingImage by AuthorPrincipal Component Analysis ( PCA ) is a popular technique to reduce the dimensions of the data and is included in most ML/DS courses under the section ‘unsupervised learning’. There are a number of blogs that explain PCA alongside YT videos, so why is this blog here?Another blog on PCA?As an ML learner who loves Math more, I found every blog on PCA incomplete. Summarizing each blog I read, I could only make a single conclusion each time, ‘PCA maximizes the…

Using scikit-learn models to perform inference in Android appsPhoto by Plann on PexelsScikit-learn is truly a package that revolutionized machine learning and data science and is still the most fundamental prerequisite for any ML/DS role. But as the domain of machine learning is making in its way from research to industry, deployment of ML models now plays a crucial role in the software development cycle.But most of us could only run scikit-learn models in Python scripts or Jupyter notebooks and only a limited number of…

Approximate distribution of the data, Dirac Delta function and much moreImage Source: AuthorEmpirical distribution is a word that you might have observed in a number of statistics textbooks, but I discovered it in the book Probabilistic Machine Learning : An Introduction by Kevin Patrick Murphy.The book provides mathematical treatments to a number of topics that are otherwise stated as ‘common sense’ in most online blogs and videos. As my interest lies in the Math behind ML ( and Statistics ), I started reading this book…