TranslatedLarge single page application with 45 pages built on vue2 + vuex.
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Curso para aprender el lenguaje de programación Python desde cero y para principiantes. 100 clases, 44 horas en vídeo, código, proyectos y grupo de chat. Fundamentos, frontend, backend, testing, IA...
A collection of tips to help take your CSS skills pro. 🕹
- css
- css3
- css-tricks
- awesome-list
A showcase of problems once hard or impossible to solve with CSS alone, now made trivially easy with Flexbox.
Standards for developing consistent, flexible, and sustainable HTML and CSS.
Translated:books: 《Go语言定制指南》 (Go Language Customization Guide) (formerly titled: Introduction to Go Syntax Trees / Open-Source Free Book / Advanced Go / Mastering Abstract Syntax Trees / Go Language AST)
CSS talks you have to see covering CSS Grid, flexbox, custom variables, performance, frameworks, tooling, and more! 📺
- css
- awesome-lists
- conference-talks
- css-grids
:eyeglasses: A curated list of CSS Flexible Box Layout Module or only Flexbox.
Symfony 7 DDD ES CQRS backend boilerplate.
- symfony
- event-sourcing
- cqrs
- cqrs-es
Admidio is a free open source user management system for websites of organizations and groups. The system has a flexible role model so that it’s possible to reflect the structure and permissions of your organization.
- php
- admidio
- organization
- user-management
zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.
- machine-learning
- python
- evolutionary-algorithms
- genetic-algorithm
A fast and flexible c++ template class for tree data structures
- c-plus-plus
- tree
- stl
- data-structure
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
- svm
- regression
- classification
- machine-learning