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This project reproduces the book 'Dive Into Deep Learning' by adapting the code from MXNet into PyTorch.
A tutorial on how to implement a hash table data structure in the C programming language.
This repository contains the book 'Large Language Models' authored by a team of researchers.
This is a course on knowledge graph research at Southeast University, not a developer tool for vibe coders.
A collection of AI-powered projects, including machine learning, deep learning, and generative AI, for developers.
This repository contains the release of the SLAM Handbook, a resource focused on SLAM (Simultaneous Localization and Mapping) for developers.
This repo contains a list of the 10,000 most common English words, useful for NLP and language modeling tasks.
A curated collection of jQuery tips and tricks to help developers improve their front-end skills.
A curated list of resources for reinforcement learning with human feedback (RLHF), a key technique for developing AI systems.
MiniOB is a lightweight database that helps developers learn the fundamentals of database systems.
A Sketch plugin that generates dummy data like avatars, names, photos, and geo data for design prototyping.
A comprehensive guide to using the RAG (Retrieval-Augmented Generation) technique for large language model applications.
A comprehensive library of scripts, snippets, and shaders for Unity developers to enhance their projects.
The Odin Project is an open-source curriculum for learning full-stack web development.
This GitHub repository contains resources and templates for mathematical modeling competitions and contests.
An open-source course to build a production-ready LLM & RAG system using best practices in LLMOps
Must-read papers on prompt-based tuning for pre-trained language models.
Comprehensive list of papers and resources on using large language models for robotics and reinforcement learning
This is a repository for the AZ-104 Microsoft Azure Administrator course, containing Bicep templates for Azure resources.
A collection of numbers and metrics every LLM developer should know for their projects.
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