Open-source alternatives to Machine Learning With Ruby

This project is a curated directory of resources, libraries, and frameworks designed to support the development, training, and deployment of neural network models. It serves as a comprehensive guide for navigating the machine learning ecosystem, providing structured access to software utilities and research materials. The directory distinguishes itself by aggregating tools across the entire machine learning lifecycle, ranging from data management and experiment tracking to production-ready model deployment. It functions as a central hub for discovering both foundational academic research and

A curated list of speech and natural language processing resources

The most cited deep learning papers

This project is a community-maintained directory that serves as a comprehensive index of software tools, frameworks, and educational materials. It functions as an open-source knowledge base, organizing diverse engineering domains and technical resources into a structured taxonomy to assist developers in discovering high-quality content. The directory distinguishes itself through a decentralized peer-review model, where independent contributors curate, verify, and update entries to ensure accuracy and relevance. All information is stored in a version-controlled, flat-file markdown format, whic

TensorFlow - A curated list of dedicated resources http://tensorflow.org

This repository serves as an educational framework for building large language models from the ground up. It provides a structured curriculum that guides learners through the end-to-end lifecycle of model development, including data processing, architecture design, and optimization. By focusing on low-level implementation, the project enables users to master the fundamental mechanics of artificial intelligence without relying on high-level abstraction frameworks. The project distinguishes itself by constructing neural network components and gradient-based optimization logic from first princip

Awesome Explainable AI (XAI) and Interpretable ML Papers and Resources

Platforms, tools, and guides for critical organizers 🌆

An awesome list of resources for specific science, technology, engineering, art, and math (STEAM) classes that students and teachers can use to supplement their learning

Curated list of 20,000+ hours and 200+ free courses with certificates in IT, CS, Design and Business.

Awesome TensorFlow.js - A curated list of dedicated resources to master TensorFlow.js

Curated list of Linguistic Resources for doing NLP & CL on Spanish

A curated list of delightful Conversational AI resources.

😎 A curated list of the Question Answering (QA)

🚀 A curated list of awesome resources for product/program managers to learn and grow.

This project serves as a comprehensive, curated directory of resources, tools, and platforms dedicated to the generative artificial intelligence ecosystem. It functions as a central hub for developers and researchers to discover the frameworks, models, and services necessary for building, deploying, and managing intelligent software applications. The directory distinguishes itself by providing a structured index of specialized tooling across several technical domains. It covers the full lifecycle of generative AI, including the development of autonomous agent systems, the implementation of re

This project is a comprehensive repository and curated index of resources, research papers, and development frameworks designed to support the construction and deployment of intelligent systems. It serves as a centralized knowledge base for developers seeking to navigate the technical landscape of artificial intelligence, ranging from foundational educational materials to specialized implementation guides. The repository distinguishes itself by providing structured directories for comparing generative artificial intelligence providers, including aggregated performance metrics, pricing data, a

This project provides a structured computer science curriculum framework designed for self-directed learners. It organizes open-access academic resources, including textbooks, lectures, and assignments, into a cohesive path that mirrors the requirements of a formal undergraduate degree. By integrating theoretical study with practical software engineering methodologies, the platform enables students to master foundational concepts and advanced technical skills independently. The curriculum distinguishes itself by utilizing a version-control-based workflow to manage the educational experience.

A curated list of deep learning resources for computer vision

Resources about public speaking

This project is a comprehensive, community-driven knowledge repository that serves as a centralized hub for data science resources. It provides a structured index of educational materials, software packages, and professional development tools designed to support both students and practitioners in navigating the data science landscape. The repository distinguishes itself through a hierarchical taxonomy that organizes a vast collection of external links into a human-readable, markdown-based document. By relying on distributed contributions, the project maintains an up-to-date snapshot of the fi

Note from the Editor: Take Two

🔬 A curated list of awesome LLMs & deep learning strategies & tools in financial market.

A tiny list limited to the best JavaScript Learning Resources

A curated list of articles that cover the software engineering best practices for building machine learning applications.

This project is a comprehensive educational curriculum and engineering handbook focused on the lifecycle of large language models. It serves as a structured knowledge base for machine learning practitioners, covering the fundamental mathematical and architectural principles of transformer-based sequence modeling, as well as the practical implementation of supervised instruction fine-tuning and preference-based model alignment. The repository distinguishes itself by providing a deep dive into advanced model composition and optimization techniques. It details methodologies for weight-space mode