The Startup CTO's Handbook, a book covering leadership, management and technical topics for leaders of software engineering teams
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A Compiler Writing Journey
The OWASP Mobile Application Security Testing Guide (MASTG) is a comprehensive manual for mobile app security testing and reverse engineering. It describes technical processes for verifying the OWASP Mobile Security Weakness Enumeration (MASWE) weaknesses, which are in alignment with the OWASP MASVS.
Translated✍🏻 Source Code Deep Dives, System Design & Engineering Blogs | Halfrost-Field (冰霜之地, "Land of Frost"): notes on source code analysis, system design, and engineering practice
- ios
- objective-c
- swift
- javascript
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
- machine-learning
- deep-learning
- deep-neural-networks
- pattern-recognition
Translated:star:Github Ranking:star: Github stars and forks ranking list. Github Top100 stars list of different languages. Automatically update daily. | Github repository ranking, automatically updated daily
- ranking
- stars
- forks
- github-rank
Dive into this repository, a comprehensive resource covering Data Structures, Algorithms, 450 DSA by Love Babbar, Striver DSA sheet, Apna College DSA Sheet, and FAANG Questions! 🚀 That's not all! We've got Technical Subjects like Operating Systems, DBMS, SQL, Computer Networks, and Object-Oriented Programming, all waiting for you.
- interview-questions
- dsa
- dbms
- sql
📚 A curated list of awesome articles, videos, and other resources to learn and practice software architecture, patterns, and principles.
- architecture
- ddd
- ddd-architecture
- system-design
TranslatedHand-written implementations of all the algorithms in Li Hang's book "Statistical Learning Methods" (《统计学习方法》)
- machine-learning-algorithms
- code
- statistical-learning-method
This repository contains a curated collection of 300+ case studies from over 80 companies, detailing practical applications and insights into machine learning (ML) system design. The contents are organized to help you easily find relevant case studies based on industry or specific ML use cases.
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
Practical Python Programming (course by @dabeaz)
- python
- pythontutorial
- tutorial
This guide details creating a secure Linux production system. OpenSCAP (C2S/CIS, STIG).
Practical samples of ASP.NET Core 11, 10, 9, 8.0, 7.0, 6.0, 5.0, 3.1, 2.2, and 2.1,projects you can use. Readme contains explanations on all projects.
- asp-net-core
- dotnet-core
- aspnet-core
- aspnetcore
How to systematically secure anything: a repository about security engineering
- security-engineering
- security-architecture
- secure-systems
- threat-modeling
A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)
A curated list of practical financial machine learning tools and applications.
Classical equations and diagrams in machine learning
A course in reinforcement learning in the wild
- reinforcement-learning
- course-materials
- deep-learning
- deep-reinforcement-learning
Go DDD example application. Complete project to show how to apply DDD, Clean Architecture, and CQRS by practical refactoring.
- go
- golang
- ddd
- clean-architecture
The repository host gives no description.
Alphabetical list of free/public domain datasets with text data for use in Natural Language Processing (NLP)
A comprehensive Domain-Driven Design example with problem space strategic analysis and various tactical patterns.
- domain-driven-design
- ddd
- ddd-architecture
- c4
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.