🧮 Partial Differential Equations: When Change Happens in More Than One Direction
đź“Ť The Mathematics Behind Heat, Waves, Fluids and Modern Engineering
numpy
3 NumPy Tricks for Numerical Performance - KDnuggets
In this article, we will cover three essential NumPy tricks to optimize your code: vectorization and broadcasting, in-place operations, and leveraging memory views instead of copies.
To optimize, you must write code using native universal functions (ufuncs) and broadcasting.
When working with very large arrays (e.g. millions of entries), allocating and garbage-collecting these temporary intermediate arrays creates substantial overhead.
Top 7 Python Libraries for Large-Scale Data Processing
This article covers Python libraries that make large-scale data processing faster, more scalable, and easier to manage across modern data workflows.
Numerical Python 3rd edition
This book demonstrates how to work with mathematical modeling and solve problems with Python's numerical, symbolic, and visualization techniques.
How to Calculate and Interpret the Covariance Matrix with NumPy
In this article, we will explore how to calculate and interpret the covariance matrix using NumPy.
How to Perform Matrix Factorization Using SVD with NumPy
In this article, we will learn how to use SVD for matrix factorization with NumPy.
NumPy vs SciPy: When to Use Each for Statistical Computing
Learn when to use NumPy vs SciPy for statistical computing with practical examples and decision frameworks.
array operations, simple statistics, and mathematical computations.
probability distributions, hypothesis testing, regression analysis, and specialized statistical procedures. Use SciPy when you need statistical inference, probability calculations, or advanced analytical techniques
10 Python One-Liners to Run Common Statistical Tests
In this article, we'll explore 10 Python one-liners that showcase the progression from basic statistical tests to sophisticated analyses.
How to Interpret Matrix Expressions — Transformations
Matrix algebra for a data scientist
Meet neograd: A Deep Learning Framework Created from Scratch Using Python a
Understanding how convolutional neural networks (CNNs) operate is essential in deep learning. However, implementing these networks, especially convolutions and gradient calculations, can be challenging. Many popular frameworks like TensorFlow and PyTorch exist, but their complex…
GPT in 60 Lines of NumPy
Implementing a GPT model from scratch in NumPy.
Beyond Numpy and Pandas: Unlocking the Potential of Lesser-Known Python Lib
3 Python libraries for scientific computation you should know as a data professional.
python-data-science-handbook/numpy at master · bjpcjp/python-data-science-handbook
Sourced from O'Reilly ebook of the same name.
Five Numpy Functions You Should Understand
How to stack your array horizontally and vertically, find unique values, split your array and some more tips to use Numpy effectively.
Using PyTorch + NumPy? You're making a mistake.
A bug that plagues thousands of open-source ML projects.
20 NumPy Operations That Every Data Scientist Should Know
NumPy forms the basis of many Python libraries in the data science domain.
NumPy Array Manipulation
A practical guide to modify the shape of arrays
Using Q-Learning in Numpy to teach an agent to play a game
Using q-learning for sequential decision making and therefore learning to play a simple game.
A Complete Beginners Guide to Matrix Multiplication for Data Science with P
Learn matrix multiplication for machine learning by following along with Python examples
NumPy indexing explained
NumPy is the universal standard for working with Numerical data in Python. Multidimensional NumPy arrays are extensively used in Pandas…
Top 3 Numpy Functions You Don’t Know About (Probably)
The ones not covered in every How-to Guide
12 Amazing Pandas & NumPy Functions
Make your day to day life easier by using these functions in your analysis