🧮 Partial Differential Equations: When Change Happens in More Than One Direction
📍 The Mathematics Behind Heat, Waves, Fluids and Modern Engineering
scipy
5 Scipy.stats Tricks for Simulating 'What If' Scenarios - KDnuggets
In this article, we will take a look under the hood of scipy.stats, exploring five essential tricks to design high-performance, rigorous simulations using only NumPy and SciPy.
A Coding Implementation to Master GPU Computing with CuPy, Custom CUDA Kernels, Streams, Sparse Matrices, and Profiling - MarkTechPost
Note: https://gist.github.com/bjpcjp/c85fa4214c1b59c3aa578a6e8e9d871c
Linear Algebra Algorithms Taxonomy: Complete Guide
Comprehensive taxonomy of linear algebra algorithms with Python & Ruby implementations. Covers vector operations, eigenvalues, SVD, decompositions, and BLAS/LAPACK routines.
5 Effect Size Measures You’ll Actually Use
Learn to calculate and interpret five essential effect size measures with Python examples and clear guidance.
Cohen’s d — the standardized difference between two group means r (correlation coefficient) — the strength of a relationship between variables Odds ratio — how much more likely an outcome is in one group than another η² (eta squared) — how much of the total variance your variable explains Cliff’s…
Reference — NetworkX 3.5 documentation
Generate random points inside a sphere
Generating random points inside a sphere of any dimension.
The customary way to generate points on a sphere in n dimensions is to generate points from an n-dimensional normal (Gaussian) distribution and normalize by dividing by the length. And the way to generate samples from an n-dimensional normal is simply to generate a list of n one-dimensional normals…
Instead, the thing to do is to set
How to Build a Complete End-to-End NLP Pipeline with Gensim: Topic Modeling, Word Embeddings, Semantic Search, and Advanced Text Analysis
explosion/spaCy: 💫 Industrial-strength Natural Language Processing (NLP) in Python
💫 Industrial-strength Natural Language Processing (NLP) in Python - explosion/spaCy
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
How to Visualize Skewness and Kurtosis in Python
In this article, you will learn how to visualize skewness and kurtosis using Python.
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 Use the Cauchy Distribution in Python
In this tutorial, we’ll learn more about the Cauchy distribution, visualize its probability density function, and learn how to use it in Python.
Calculating the Uncertainty Coefficient (Theil’s U) in Python
A measure of correlation between discrete (categorical) variables
A Whimsical Journey Through Wait Times
From Microwave Countdowns to Never-Ending Call Holds, with Python
Data Science at the Command Line, 2e
This is an overview of all the command-line tools discussed in this book. This includes binary executables, interpreted scripts, and Z Shell builtins and keywords. For each command-line tool, the...
SciPy 1.13.0 Release Notes — SciPy v1.14.0.dev Manual
Bounded Kernel Density Estimation
Learn how Kernel Density Estimation works and how you can adjust it to better handle bounded data, like age, height, or price
3 Key Encoding Techniques for Machine Learning: A Beginner-Friendly Guide
How should we choose between label, one-hot, and target encoding?
30 Python Libraries that I Often Use
30 Python libraries to solve most AI problems, including GenAI, data videos, synthetization, model evaluation, computer vision and more.
CoAxLab/DeBaCl: Density Based Clustering (DeBaCl) Toolbox
Density Based Clustering (DeBaCl) Toolbox.
skfolio/skfolio
Python library for portfolio optimization built on top of scikit-learn - skfolio/skfolio
luong-komorebi/Awesome-Linux-Software
🐧 A list of awesome Linux softwares .
Install scikit-learn in Jupyter Lab
Shared via ChatGPT
How to Implement Hierarchical Clustering for Direct Marketing Campaigns— wi
Understand the ins and outs of hierarchical clustering, and how it applies to marketing campaign analysis in the banking industry.
Variational Inference: The Basics
Implementing variational inference from scratch
Deploying Falcon-7B Into Production
Running Falcon-7B in the cloud as a microservice
Introduction to Vector Similarity Search
Learn what vector search is and the metrics pertinent to decide the distance (or similarity) between objects.
The Basics of Anomaly Detection
Basics of anomaly detection, its use-cases, and an implementation of simple yet powerful algorithm in Python
Scalar Quantization and Product Quantization - Zilliz blog
A hands-on dive into scalar quantization (integer quantization) and product quantization with Python.
skops: a new library to improve scikit-learn in production
There are various challenges in MLOps and model sharing, including, security and reproducibility. To tackle these for scikit-learn models, we've developed a new open-source library: skops. In this article, I will walk you through how it works and how to use it with an end-to-end…
Why WGANs beat GANs: A journey from KL divergence to Wasserstein loss
Wasserstein distance helps WGANs outperform vanilla GANs and VAEs. This post explains why so using some easy math.
Introduction to Embedding, Clustering, and Similarity
Introduction to key elements of ML and Autoencoders: Embedding, Clustering, and Similarity.
Neighborhood Analysis, KD-Trees, and Octrees for Meshes and Point Clouds in
How to use Python libraries like Open3D, PyVista, and Vedo for neighborhood analysis of point clouds and meshes through KD-Trees/Octrees
Real-world website visitor forecast with Facebook Prophet: A Complete Tutor
As a data analyst at Microsoft, I must investigate and understand time-series data every day. Besides looking at some key performance…
scikit-learn/README.md at master · bjpcjp/scikit-learn
Updates in progress. Jupyter workbooks will be added as time allows. - bjpcjp/scikit-learn
python-data-science-handbook/scikit/SciKit-Feature-Engineering.ipynb at master · bjpcjp/python-data-science-handbook
Sourced from O'Reilly ebook of the same name.
Survival Analysis in Python: A Quick Guide to The Weibull Analysis
A Quick Guide to The Weibull Analysis
W3Schools.com
W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more.
python-data-science-handbook/scikit/SciKit-Kernel-Density-Estimation.ipynb at master · bjpcjp/python-data-science-handbook
Sourced from O'Reilly ebook of the same name.
python-data-science-handbook/scikit/SciKit-Principal-Component-Analysis.ipynb at master · bjpcjp/python-data-science-handbook
Sourced from O'Reilly ebook of the same name.
python-data-science-handbook/numpy at master · bjpcjp/python-data-science-handbook
Sourced from O'Reilly ebook of the same name.
spaCy_hello_world/spaCy_101.ipynb at master · bjpcjp/spaCy_hello_world
Beginner's tour of spaCy v2.0.
A ~5 minute guide to Numba — Numba 0.52.0.dev0+274.g626b40e-py3.7-linux-x86_64.egg documentation
Frequencies%20 presentation
leandromoreira/digital_video_introduction: A hands-on introduction to video technology: image, video, codec (av1, vp9, h265) and more…
A hands-on introduction to video technology: image, video, codec (av1, vp9, h265) and more (ffmpeg encoding). Translations: 🇺🇸 🇨🇳 🇯🇵 🇮🇹 🇰🇷 🇷🇺 🇧🇷 🇪🇸 - leandromoreira/digital_video_introduction
Probability Distributions with Python’s SciPy
How to Model random Processes with Distributions and Fit them to Observational Data
Scikit-Learn Version 1.0
For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.0. Legend for changelogs something big that you couldn’t do before., something t...
Conda: essential concepts and tricks
for beginners as well as advanced users
Why are most climate models in Fortran
An examination of the software that powers modern Climate simulations.