r/MistralAI points to a Reddit community or thread with discussion, updates, troubleshooting, or shared resources.
An article by Cavin on Deep Markets discussing machine-generated content and creativity. It describes technology as leverage over creation: writing, the printing press, the internet, digital tools and machine learning. It also covers synthetic text from large language models such as OpenAI's GPT-3, published in summer 2020.
Text Mining with R: A Tidy Approach is a website for a guide to text analysis within the tidy data framework, using the tidytext package and other tidy tools. It is written by Julia Silge and David Robinson and licensed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License.
Stanford MLSys Seminar Series is a seminar series on the frontier of machine learning and systems. Its 2025 "Hidden Layer" series schedule lists talks, and earlier limited series covered systems for machine learning and foundation models. Stanford students could sign up for associated classes, and an email list provides speaker and livestream notifications.
A personal notes site with writings on topics the author is thinking about, learning, and working on. It includes an upcoming entry about building Atlas, described as an AI tool for thought, and an archive of earlier posts.
VLFeat is an open-source library of computer vision algorithms focused on image understanding and local feature extraction and matching. Algorithms include SIFT, MSER, Fisher Vector, VLAD, k-means, SLIC superpixels, and SVM training. It is written in C with MATLAB interfaces and supports Windows, Mac OS X, and Linux.
Neuroph is a lightweight, open source Java neural network framework released under the Apache 2.0 license. It provides a Java library and a GUI editor, Neuroph Studio, based on the NetBeans Platform, for creating, training and saving neural networks. It supports architectures including Perceptron, Multi Layer Perceptron, Hopfield, Kohonen and RBF networks, and both supervised and unsupervised learning rules.
Community notes
Be the first to share how this project worked for you.
Sign in with a verified account to leave a review or join the discussion.
Sign in to reviewKeep exploring
An article by Cavin on Deep Markets discussing machine-generated content and creativity. It describes technology as leverage over creation: writing, the printing press, the internet, digital tools and machine learning. It also covers synthetic text from large language models such as OpenAI's GPT-3, published in summer 2020.
Text Mining with R: A Tidy Approach is a website for a guide to text analysis within the tidy data framework, using the tidytext package and other tidy tools. It is written by Julia Silge and David Robinson and licensed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States License.
Stanford MLSys Seminar Series is a seminar series on the frontier of machine learning and systems. Its 2025 "Hidden Layer" series schedule lists talks, and earlier limited series covered systems for machine learning and foundation models. Stanford students could sign up for associated classes, and an email list provides speaker and livestream notifications.
A personal notes site with writings on topics the author is thinking about, learning, and working on. It includes an upcoming entry about building Atlas, described as an AI tool for thought, and an archive of earlier posts.
VLFeat is an open-source library of computer vision algorithms focused on image understanding and local feature extraction and matching. Algorithms include SIFT, MSER, Fisher Vector, VLAD, k-means, SLIC superpixels, and SVM training. It is written in C with MATLAB interfaces and supports Windows, Mac OS X, and Linux.
Neuroph is a lightweight, open source Java neural network framework released under the Apache 2.0 license. It provides a Java library and a GUI editor, Neuroph Studio, based on the NetBeans Platform, for creating, training and saving neural networks. It supports architectures including Perceptron, Multi Layer Perceptron, Hopfield, Kohonen and RBF networks, and both supervised and unsupervised learning rules.
