Hey, I'm Kamrun Nahar. I write about the stuff most tutorials make unnecessarily painful. Statistical theory, machine learning algorithms, deep learning, and Python implementations.

My thing is simple: I take topics like Bayesian inference, MCMC sampling, BERT fine-tuning, gradient descent, and hypothesis testing, and I explain them the way I wish someone had explained them to me. With real analogies, working code, and zero hand-waving.

What I write about: → Core statistics (probability, distributions, Bayes, CLT, hypothesis testing, time series) → Machine learning (gradient descent, kernel methods, Gaussian processes, SVMs) → Deep learning (BERT, transformers, fine-tuning strategies) → All of it with Python: NumPy, scikit-learn, statsmodels, PyTorch

Why I write here: Because I spent too many hours in grad school reading papers that could have been one clear paragraph. Somebody should fix that. I'm trying to be that somebody.

If you're a self-taught developer, a grad student drowning in notation, or a career-switcher trying to actually understand what's under the hood, you're who I write for.

Find me elsewhere: → Email: knahar.official@gmail.com