3 Repos
Instructional guides and references for performing high-performance mathematical operations on multi-dimensional arrays.
Distinct from NumPy-Compatible Frameworks: The candidates focus on specific integration layers or distributed workflows, whereas this is a general educational reference for NumPy.
Explore 3 awesome GitHub repositories matching scientific & mathematical computing · Numerical Computing References. Refine with filters or upvote what's useful.
This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data. The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution p
Provides a comprehensive reference for high-performance vectorized operations and linear algebra using NumPy.
This project is a collection of educational notes and tutorials focused on Python programming, scientific computing, and data analysis. It serves as a reference for learning language basics, advanced techniques, and object-oriented design. The materials include implementation guides for building linear, logistic, and convolutional neural networks using symbolic graph frameworks. It also provides instruction on manipulating and visualizing structured data frames and performing complex mathematical operations through numerical libraries. The repository includes a system for converting interact
Provides an instructional guide for performing high-performance mathematical operations on multi-dimensional arrays.
This repository serves as an educational collection of Python implementations for fundamental machine learning algorithms and statistical models. It provides a structured environment for learning core concepts through interactive computational documents that combine live code, narrative text, and data visualizations. The codebase focuses on predictive modeling development, offering instructional examples for building and evaluating regression, classification, and neural network models. It utilizes standardized data science library interfaces to demonstrate how to implement and execute these a
Provides instructional references for performing high-performance mathematical operations on multi-dimensional arrays.