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Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia.
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A chat LLM based on the state-space model architecture.
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A Julia package for manifold learning and nonlinear dimensionality reduction.
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A user-friendly Python toolkit for open source intelligence, providing key features such as image geolocation, social media profiling, email breach checks, domain lookup, metadata extraction, Google dorking, Wayback Machine queries, IP geolocation with blacklist checks, reverse image search, among others.
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A Clojure library of optimisation and control theory tools and convenience functions based on Neanderthal.
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Application-oriented deep reinforcement learning framework addressing real-world decision problems.
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Small Language Model tailored for edge devices.
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MegEngine is a fast, scalable and easy-to-use deep learning framework, with auto-differentiation.
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The memory layer for Personalized AI.
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Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API.
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Flexible Deep Learning Framework in Julia.
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Official repository to use and implement LLaMA 3.1 models, Meta's state-of-the-art large language models.
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An open source robotics benchmark for meta- and multi-task reinforcement learning.
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Neural networks (boltzmann machines, feed-forward and recurrent nets), Gaussian Processes.
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JARVIS, a system to connect LLMs with ML community.
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General technology for enabling AI capabilities w/ LLMs and MLLMs.
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Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"s.
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Integrate LLM technology quickly and easily into your apps.
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LLM-powered multiagent persona simulation for imagination enhancement and business insights.
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MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
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Intuitive convenience tooling for lightning-fast, efficient development and ensuring quality in LLM-based applications.
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C, C++, and Python tools for named entity recognition and relation extraction.
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A Julia package for fitting (statistical) mixed-effects models.
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500+ ML/AI interview Q&A with runnable code — covers ML fundamentals, deep learning, NLP, PyTorch, scikit-learn pipelines, and system design.