:bathtub: Clean Code concepts and tools adapted for .NET
Repository Index
47,133 repositories indexed
265–288 of 1,052 · Most stars
Lisp code for the textbook "Paradigms of Artificial Intelligence Programming"
A list of command-line tools for manipulating structured text data
JavaScript talks you have to see 👀 on functional programming, performance, frameworks, React, debugging, leveling up, and more!
- javascript
- react
- learning-resources
- awesome-list
A free, online learning platform to make quality education accessible for all.
🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.
- python
- utilities
- cache
- data-structures
A collection of public resources about how software companies test their software
- testing
- companies
- testing-practices
- quality
Examples built with TensorFlow.js
🚀 Ready-to-run Prisma example projects
Principles of writing consistent, idiomatic CSS.
A list of interesting payloads, tips and tricks for bug bounty hunters.
A standard style for README files
- readme
- readme-standard
- standard
- docs
180+ Algorithm & Data Structure Problems using C++
ECMAScript 6: Feature Overview & Comparison
The repository host gives no description.
😎🏖️🐬 Your new, 𝙧𝙞𝙙𝙤𝙣𝙠𝙪𝙡𝙞𝙘𝙞𝙤𝙪𝙨𝙡𝙮 smart clipboard manager
A repo containing samples tied to new functionality in each release of Google Chrome.
An Open-Source Collection of Flash Cards to Help You Preparing Your Algorithms & Data Structures and System Design Interviews 💯
- algorithms
- data-structures
- array
- bit-manipulation
:dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
- benchmark-ips
- performance-optimization
- ruby
- hacktoberfest
Ultra-fast bootstrapping with Angular and Electron :speedboat:
Example projects using the AWS CDK
- cdk
- cdk-examples
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
React: CSS in JS techniques comparison
- cssinjs
- css-in-js
A curated list of awesome CMake resources, scripts, modules and examples.
- cmake
- cmake-resources
- cmake-scripts
- cmake-modules