P-tuning v2 is an optimized deep prompt tuning strategy that applies continuous prompts to every layer input of a pretrained transformer. It is described as comparable to fine-tuning across scales and tasks.
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.
Hong Jing (Jingles) is a data scientist and Alibaba PhD student who writes about machine learning and neuroscience. The site collects blog articles on topics such as reinforcement learning agents, learned optimizers, convolutional layers, transformer models and decoding brain signals.
Tune is a Python library for experiment execution and hyperparameter tuning at any scale. It supports machine learning frameworks including PyTorch, XGBoost, and TensorFlow and Keras, and runs algorithms such as Population Based Training (PBT) and HyperBand/ASHA. It also integrates with hyperparameter optimization tools including Ax, BayesOpt, BOHB, Nevergrad, and Optuna.
Daruy provides machine-learning models, research, training resources, or evaluation tools.
DagsHub is an AI platform for managing the machine learning lifecycle, covering dataset curation and annotation, experiment tracking, and model management. It supports unstructured and multimodal data such as text, images, audio, video, and documents. The platform is built on open source tools and formats. DagsHub states it does not currently provide compute resources and relies on the user's own infrastructure.
Persuva generates Shopify product pages, landing pages and advertorials in one click, aimed at dropshippers. The site lists an audience analyser among its features and states the product is built and optimised for stores on Shopify. Pricing details with explicit plans are not stated in the supplied documents.
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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.
Hong Jing (Jingles) is a data scientist and Alibaba PhD student who writes about machine learning and neuroscience. The site collects blog articles on topics such as reinforcement learning agents, learned optimizers, convolutional layers, transformer models and decoding brain signals.
Tune is a Python library for experiment execution and hyperparameter tuning at any scale. It supports machine learning frameworks including PyTorch, XGBoost, and TensorFlow and Keras, and runs algorithms such as Population Based Training (PBT) and HyperBand/ASHA. It also integrates with hyperparameter optimization tools including Ax, BayesOpt, BOHB, Nevergrad, and Optuna.
Daruy provides machine-learning models, research, training resources, or evaluation tools.
DagsHub is an AI platform for managing the machine learning lifecycle, covering dataset curation and annotation, experiment tracking, and model management. It supports unstructured and multimodal data such as text, images, audio, video, and documents. The platform is built on open source tools and formats. DagsHub states it does not currently provide compute resources and relies on the user's own infrastructure.
Persuva generates Shopify product pages, landing pages and advertorials in one click, aimed at dropshippers. The site lists an audience analyser among its features and states the product is built and optimised for stores on Shopify. Pricing details with explicit plans are not stated in the supplied documents.
