Tutorials, assignments, and competitions for MIT Deep Learning related courses.
- deep-learning
- mit
- self-driving-cars
- deep-reinforcement-learning
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Tutorials, assignments, and competitions for MIT Deep Learning related courses.
The Concise TypeScript Book: A Concise Guide to Effective Development in TypeScript. Free and Open Source.
TranslatedA comprehensive summary of computer fundamentals interview questions (computer networks / operating systems / databases / Git...), including detailed follow-up questions and answers; everything is in the form of [question + follow-up + answer], ready to use right away and aimed directly at interviews at major internet companies; can be used for mock interviews, review before interviews, and preparing quickly for interviews in a short time...
How to systematically secure anything: a repository about security engineering
Data science interview questions and answers
Go (Golang) Clean Architecture based on Reading Uncle Bob's Clean Architecture
🌄 The Cloud Native Interactive Landscape filters and sorts hundreds of projects and products, and shows details including GitHub stars, funding, first and last commits, contributor counts and headquarters location.
A curated list of tools for incident response
The Generative AI Landscape - A Collection of Awesome Generative AI Applications
A curated list of Web Performance Optimization.
A curated list of practical financial machine learning tools and applications.
An easy-to-read, quick reference for JS best practices, accepted coding standards, and links around the Web
A curated list of amazingly awesome Home Assistant resources.
AI,Opensource,Database,Business,Finance,Minds. git clone --depth 1 https://github.com/digoal/blog
An awesome list of awesome YouTubers that teach about technology. Tutorials about web development, computer science, machine learning, game development, cybersecurity, and more.
:bathtub: Clean Code concepts and tools adapted for .NET
Clean Architecture template for Golang services
Lisp code for the textbook "Paradigms of Artificial Intelligence Programming"
Learning RxJava for Android by example
Toturials coming with the "data science roadmap" picture.
TranslatedOpen-source manuscript of 《深入理解 AI Infra:量化分析与系统设计》 (In-Depth Understanding of AI Infra: Quantitative Analysis and System Design), by 李博杰 (Li Bojie): starting from hardware constraints and model architecture, it quantitatively derives the system design of LLM inference and training. Includes the full text of the book, a PDF, and companion calculation tools and experiments
A curated list of cryptography resources and links.
JavaScript talks you have to see 👀 on functional programming, performance, frameworks, React, debugging, leveling up, and more!
🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.