Explores NF4 and FP4 4-bit floating point formats for LLM weight quantization, focusing on Gaussian-distributed weights.
Explores using NumPy as a synth engine to generate instrument sounds like plucked strings and tabla via math and DSP algorithms.
Explains the orthogonal Procrustes problem: finding a rotation matrix to align one matrix with another using SVD, with Python code.
A guide to accelerating NumPy computations using parallel processing with thread pools and Numba for optimized performance.
A technical guide to implementing a GPT model from scratch using only 60 lines of NumPy code, including loading pre-trained GPT-2 weights.
Analyzes performance limitations in scikit-learn due to CPython internals, memory hierarchy issues, and lack of low-level data structures.
A technical guide on computing distance matrices using NumPy, focusing on Euclidean distance and its application in machine learning algorithms like k-Nearest Neighbors.
A tutorial on using NumPy for numerical arrays and Matplotlib for data visualization in Python, aimed at scientific computing and machine learning.
An introduction to scientific computing in Python using NumPy for numerical arrays and Matplotlib for data visualization.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
A guide to setting up a development environment for SciPy on Mac OS, including installing dependencies and using development mode.
A tutorial on using Cython to optimize slow numerical Python code, demonstrated with an Ising Model simulation.
Gnocchi 4.1 release notes highlight new Redis notification support, a Pandas to Numpy migration, and a powerful new aggregates API.
A review and tips for Georgia Tech's OMSCS CS6476 Computer Vision course, covering content, assignments, and personal experience.
A guide to optimizing a non-trivial algorithm (NUFFT) in Python using NumPy and Numba, comparing performance to a Fortran implementation.
A cheat sheet comparing matrix operations across MATLAB/Octave, Python (NumPy), R, and Julia for scientific computing and data analysis.
A tutorial on implementing Conway's Game of Life in Python using NumPy and SciPy, with visualization via matplotlib animations.
A 2016 article analyzing the slow adoption of Python 3 in the scientific community, now outdated with a recommendation to just use Python 3.
