Mahima Pushkarna is making data easier to understand

Favorite Five years ago, information designer Mahima Pushkarna joined Google to make data easier to understand. As a senior interaction designer on the People + AI Research (PAIR) team, she designed Data Cards to help everyone better understand the contexts of the data they are using. The Data Cards Playbook

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Shared by Google AI Technology June 30, 2022

Hyperparameter optimization for fine-tuning pre-trained transformer models from Hugging Face

Favorite Large attention-based transformer models have obtained massive gains on natural language processing (NLP). However, training these gigantic networks from scratch requires a tremendous amount of data and compute. For smaller NLP datasets, a simple yet effective strategy is to use a pre-trained transformer, usually trained in an unsupervised fashion

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Shared by AWS Machine Learning June 29, 2022

Reducing gender-based harms in AI with Sunipa Dev

Favorite Natural language processing (NLP) is a form of artificial intelligence that teaches computer programs how to take in, interpret, and produce language from large data sets. For example, grammar checkers use NLP to come up with grammar suggestions that help people write grammatically correct phrases. But as Google’s AI

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Shared by Google AI Technology June 29, 2022

Deep demand forecasting with Amazon SageMaker

Favorite Every business needs the ability to predict the future accurately in order to make better decisions and give the company a competitive advantage. With historical data, businesses can understand trends, make predictions of what might happen and when, and incorporate that information into their future plans, from product demand

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Shared by AWS Machine Learning June 28, 2022

New built-in Amazon SageMaker algorithms for tabular data modeling: LightGBM, CatBoost, AutoGluon-Tabular, and TabTransformer

Favorite Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various

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Shared by AWS Machine Learning June 28, 2022