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This article teaches new techniques and tricks for using universal functions (ufuncs) in NumPy, a Python library for numerical computing.
This blog post traces the execution of NumPy's np.add function from Python down to C, explaining the internal machinery and SIMD optimization involved in numerical addition.
The article discusses efforts to improve NumPy's performance on free-threaded Python (Python without the Global Interpreter Lock), enabling better parallelism and scalability.
A practical guide to Python for engineers and scientists, covering NumPy, SciPy, Matplotlib, and more with exercises and real-world applications.
Promotional tweet about learning NumPy for Python, linking to the official guide book and the numpy.org/learn site.
A quantitative finance expert systematically explained the practical use of Python in quantitative finance at PyData, covering tools such as NumPy, SciPy, Pandas, Numba, and recommended the open-source library DX Analytics and the resource list awesome-quant.
A GitHub repository implementing fundamental machine learning algorithms from scratch using plain NumPy, designed to help learners understand the inner workings of algorithms by focusing on clarity over performance. It covers supervised, unsupervised, deep learning, and reinforcement learning topics.
A GitHub repo offering build-from-scratch machine learning tutorials using NumPy, organized by categories, with an implementation-first approach.
The article compares two methods for normalizing RGB values (dividing by 255 vs 256) and explains the consequences for floating-point conversion and rounding, including uneven bin widths at the extremes.