Classical equations and diagrams in machine learning
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🔍 A collection of interesting, funny, and depressing search queries to plug into shodan.io 👩💻
- shodan
- infosec
- pentesting
- iot
:bathtub: Clean Code concepts and tools adapted for .NET
Learning RxJava for Android by example
- rxjava
- java
- learning-rxjava
- example
Learn You The Node.js For Much Win! An intro to Node.js via a set of self-guided workshops.
- workshopper
- nodeschool
- nodejs
- learn-to-code
Step-by-step guide through the abstract and complex universe of Fragment Shaders.
A collection of public resources about how software companies test their software
- testing
- companies
- testing-practices
- quality
Kubernetes application example tutorials
One webpage for every book ever published!
- internet-archive
- open-source
- books
- library-catalogue
A list of interesting payloads, tips and tricks for bug bounty hunters.
Go DDD example application. Complete project to show how to apply DDD, Clean Architecture, and CQRS by practical refactoring.
- go
- golang
- ddd
- clean-architecture
A one pager for emojis on Campfire and GitHub
😎🏖️🐬 Your new, 𝙧𝙞𝙙𝙤𝙣𝙠𝙪𝙡𝙞𝙘𝙞𝙤𝙪𝙨𝙡𝙮 smart clipboard manager
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
😎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.
A collection of AWESOME things about domain adaptation
- transfer-learning
- domain-adaptation
- adversarial-learning
- image-translation
TranslatedA beginner's guide to FDE (Forward Deployed Engineer) from scratch (based on the framework of Fan Bing's original book 增长黑客 (Growth Hacker, "Growth Hacking"))
The Mobile App Pentest cheat sheet was created to provide concise collection of high value information on specific mobile application penetration testing topics.
Awesome XSS stuff
- xss
- payload
- xss-payloads
- payload-list
A curation of prompts, plugins & other Fish treasures 🐚💎
A curated list about Audio Visualization.
Curated list of awesome GAN applications and demo
- generative-adversarial-network
- curated-list
- applications
- demonstration