Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data. It includes built-in datasets, prediction algorithms such as SVD and NMF, similarity measures, and cross-validation tools. The documentation states that Surprise does not support implicit ratings or content-based information.
MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. It includes tools for converting text to features, classifiers such as Naïve Bayes, Maximum Entropy and Decision Trees, sequence tagging with Hidden Markov Models and Conditional Random Fields, and sampling-based topic model implementations.
Annotated talk slides by Arvind Narayanan and Sayash Kapoor of Princeton University, dated October 4, 2023, on evaluating large language models. The slides discuss three hard problems in LLM evaluation: prompt sensitivity, construct validity and contamination, and examine questions about ChatGPT's behavior over time and its political bias.
Chain of Thought is a podcast hosted by Conor Bronsdon about AI and software infrastructure, featuring conversations with people building AI agents and infrastructure. Guests include people from NVIDIA, Google DeepMind, AMD, Databricks and Vercel. The show has 78 episodes and is available on YouTube, Spotify, Apple Podcasts and RSS.
The MXNet Clojure package brings GPU computing and deep learning to the Clojure programming language. It enables tensor and matrix computation with multiple GPUs in Clojure, and allows constructing and customizing deep learning models for tasks such as image classification and data science challenges.
Charisma.ai is a conversational AI platform for building interactive scenarios for online training and campaigns. It offers realistic conversation simulations and real-time analytics on engagement, with publication to web, VR or game engine experiences through SDKs. Plans include a pay-as-you-go option and an enterprise tier with development options.
Apache SINGA is an Apache Top Level Project focused on distributed training of deep learning and machine learning models. It supports data parallel training across multiple GPUs on a single node or across different nodes, automatic gradient calculation, and loading and saving ONNX format models. Models trained with SINGA can be queried as stored procedures in an RDBMS.
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MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. It includes tools for converting text to features, classifiers such as Naïve Bayes, Maximum Entropy and Decision Trees, sequence tagging with Hidden Markov Models and Conditional Random Fields, and sampling-based topic model implementations.
Annotated talk slides by Arvind Narayanan and Sayash Kapoor of Princeton University, dated October 4, 2023, on evaluating large language models. The slides discuss three hard problems in LLM evaluation: prompt sensitivity, construct validity and contamination, and examine questions about ChatGPT's behavior over time and its political bias.
Chain of Thought is a podcast hosted by Conor Bronsdon about AI and software infrastructure, featuring conversations with people building AI agents and infrastructure. Guests include people from NVIDIA, Google DeepMind, AMD, Databricks and Vercel. The show has 78 episodes and is available on YouTube, Spotify, Apple Podcasts and RSS.
The MXNet Clojure package brings GPU computing and deep learning to the Clojure programming language. It enables tensor and matrix computation with multiple GPUs in Clojure, and allows constructing and customizing deep learning models for tasks such as image classification and data science challenges.
Charisma.ai is a conversational AI platform for building interactive scenarios for online training and campaigns. It offers realistic conversation simulations and real-time analytics on engagement, with publication to web, VR or game engine experiences through SDKs. Plans include a pay-as-you-go option and an enterprise tier with development options.
Apache SINGA is an Apache Top Level Project focused on distributed training of deep learning and machine learning models. It supports data parallel training across multiple GPUs on a single node or across different nodes, automatic gradient calculation, and loading and saving ONNX format models. Models trained with SINGA can be queried as stored procedures in an RDBMS.
