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Flexible Deep Learning Framework in Julia.
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github.com
Official repository to use and implement LLaMA 3.1 models, Meta's state-of-the-art large language models.
github.com
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.
github.com
500+ ML/AI interview Q&A with runnable code — covers ML fundamentals, deep learning, NLP, PyTorch, scikit-learn pipelines, and system design.
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No need to keep checking your training, just one import line and you'll know the second it's done.
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Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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A modular active learning framework for Python, built on top of scikit-learn.
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Visual testing tool for MCP servers.
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OpenAI compatible API for LLMs and embeddings (LLaMA, Vicuna, ChatGLM and many others).
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Global, black box optimization engine for real world metric optimization by Yelp.
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List of molecular design using Generative AI and Deep Learning.
github.com
Montague is a semantic parsing library for Scala with an easy-to-use DSL.
github.com
Machine learning model serving framework with dynamic batching and pipelined stages, provides an easy-to-use Python interface.