brexhq/prompt-engineering is a GitHub repository containing Brex's Prompt Engineering Guide. It provides tips and tricks for working with Large Language Models such as OpenAI's GPT-4, with content covering the definition of large language models, an incomplete history of language models, and the concept of prompts.
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.
The Packrift Packaging Optimization Benchmark Corpus is a publicly available dataset generated from 1,000 exact-spec Packrift feed records, containing 24,000 SKU-specific benchmark pages across 24 operational page types for packaging optimization workflows, with public source ledgers, manifests, metadata, and solver fixtures to support auditability.
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.
The Packrift Packaging Optimization Benchmark Corpus is a publicly available dataset generated from 1,000 exact-spec Packrift feed records, containing 24,000 SKU-specific benchmark pages across 24 operational page types for packaging optimization workflows, with public source ledgers, manifests, metadata, and solver fixtures to support auditability.
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.
