Go microservice template for Kubernetes
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Alphabetical list of free/public domain datasets with text data for use in Natural Language Processing (NLP)
A comprehensive curated list of available Bug Bounty & Disclosure Programs and Write-ups.
A curated list of fuzzing resources ( Books, courses - free and paid, videos, tools, tutorials and vulnerable applications to practice on ) for learning Fuzzing and initial phases of Exploit Development like root cause analysis.
- awesome-list
- awesome
- fuzzing
- fuzzing-framework
Curated list of 20,000+ hours and 200+ free courses with certificates in IT, CS, Design and Business.
😎🏖️🐬 Your new, 𝙧𝙞𝙙𝙤𝙣𝙠𝙪𝙡𝙞𝙘𝙞𝙤𝙪𝙨𝙡𝙮 smart clipboard manager
A curated list of awesome Swift frameworks, libraries and software.
- swift-language
- swift
- swift-library
- swift-framework
A repo containing samples tied to new functionality in each release of Google Chrome.
A comprehensive Domain-Driven Design example with problem space strategic analysis and various tactical patterns.
- domain-driven-design
- ddd
- ddd-architecture
- c4
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
common data analysis and machine learning tasks using python
- data-science
- python
- python-tutorial
- data-scientists
:fire: Learn some Swift
- swift
- swift-education
- awesome
- awesome-list
Roadmap to becoming a game developer in 2022
A curated list of awesome infosec courses and training resources.
:dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
- benchmark-ips
- performance-optimization
- ruby
- hacktoberfest
From the basics to slightly more interesting applications of Tensorflow
:art: A curated contents of amazing CSS :)
😎Awesome GIS is a collection of geospatial related sources, including cartographic tools, geoanalysis tools, developer tools, data, conference & communities, news, massive open online course, some amazing map sites, and more.
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.
An authoritative list of awesome devsecops tools with the help from community experiments and contributions.
A curated list of awesome iOS animation, including Objective-C and Swift libraries
- ios-animation
- swift-animation
- animations
React: CSS in JS techniques comparison
- cssinjs
- css-in-js
A collection of AWESOME things about domain adaptation
- transfer-learning
- domain-adaptation
- adversarial-learning
- image-translation
A curated list of awesome CMake resources, scripts, modules and examples.
- cmake
- cmake-resources
- cmake-scripts
- cmake-modules