Search Results for "computer science projects"

Computational Thinking is an open source MIT course repository that teaches computational problem-solving through the Julia programming language. The course integrates mathematics, computing, and real-world applications into a unified curriculum, making it suitable for students across science, engineering, and data-driven fields. It emphasizes learning how to translate problems into computational terms and developing algorithms and models to analyze them effectively. Using Julia, the course highlights both mathematical reasoning and practical coding, bridging the gap between theory and application. The materials include lectures, notebooks, exercises, and projects that encourage experimentation and discovery. ...

Unlike traditional package managers, which install and manage a single global set of packages, Pkg is designed around “environments”: independent sets of packages that can be local to an individual project or shared and selected by name. The exact set of packages and versions in an environment is captured in a manifest file which can be checked into a project repository and tracked in version control, significantly improving reproducibility of projects. If you’ve ever tried to run code you...

FastAI.jl is a Julia library for training state-of-the-art deep learning models. From loading datasets and creating data preprocessing pipelines to training, FastAI.jl takes the boilerplate out of deep learning projects. It equips you with reusable components for every part of your project while remaining customizable at every layer. FastAI.jl comes with support for common computer vision and tabular data learning tasks, with more to come.

Pythonidae is a curated collection of scientific programming resources in Python, designed to support research and development across a wide range of disciplines. The repository organizes tools and libraries into domain-specific categories, including mathematics, statistics, machine learning, artificial intelligence, biology, chemistry, physics, earth sciences, and supercomputing. It also covers practical areas such as build automation, databases, APIs, computer graphics, and utilities,...