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How to tackle lack of data: an overview on transfer learning

Data Science Blog

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves. That is, is giving supervision to adjust via.

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Pushing the Boundaries of AI-based Lossy Compression

IBM Data Science in Practice

For instance, the Sentinel Data Access System has recorded 586 PiB in downloads over recent years. From Data Cubes to Embeddings During the development phase in March, participants will pretrain their encoders using self-supervised learning methods that underpin neural compression and EO foundation models.

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Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0

AWS Machine Learning Blog

These models are trained using self-supervised learning algorithms on expansive datasets, enabling them to capture a comprehensive repertoire of visual representations and patterns inherent within pathology images. script that automatically downloads and organizes the data in your EFS storage.

AWS 101
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Build an email spam detector using Amazon SageMaker

AWS Machine Learning Blog

We walk you through the following steps to set up our spam detector model: Download the sample dataset from the GitHub repo. Download the dataset Download the email_dataset.csv from GitHub and upload the file to the S3 bucket. Set the learning mode hyperparameter to supervised. Prepare the data for the model.

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Supervised learning is great — it's data collection that's broken

Explosion

Prodigy features many of the ideas and solutions for data collection and supervised learning outlined in this blog post. It’s a cloud-free, downloadable tool and comes with powerful active learning models. Transfer learning and better annotation tooling are both key to our current plans for spaCy and related projects.

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Build a Hugging Face text classification model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

This supervised learning algorithm supports transfer learning for all pre-trained models available on Hugging Face. The pre-trained model tarballs have been pre-downloaded from Hugging Face and saved with the appropriate model signature in S3 buckets, such that the training job runs in network isolation.

Algorithm 121
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Offline RL Made Easier: No TD Learning, Advantage Reweighting, or Transformers

BAIR

A demonstration of the RvS policy we learn with just supervised learning and a depth-two MLP. It uses no TD learning, advantage reweighting, or Transformers! Offline reinforcement learning (RL) is conventionally approached using value-based methods based on temporal difference (TD) learning.