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

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 on

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Shared by AWS Machine Learning June 29, 2022

Deep demand forecasting with Amazon SageMaker

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 to

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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

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 types

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Shared by AWS Machine Learning June 28, 2022