Remove 2020 Remove Clustering Remove Supervised Learning
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Against LLM maximalism

Explosion

Once you’re past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability than in-context learning for non-generative tasks — tasks where there is a specific right answer that you want the model to find. That’s not a path to improvement.

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Conformer-2: a state-of-the-art speech recognition model trained on 1.1M hours of data

AssemblyAI

Building on In-House Hardware Conformer-2 was trained on our own GPU compute cluster of 80GB-A100s. To do this, we deployed a fault-tolerant and highly scalable cluster management and job scheduling Slurm scheduler, capable of managing resources in the cluster, recovering from failures, and adding or removing specific nodes.

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Create and fine-tune sentence transformers for enhanced classification accuracy

AWS Machine Learning Blog

Sentence transformers are powerful deep learning models that convert sentences into high-quality, fixed-length embeddings, capturing their semantic meaning. These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval.

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When Scripts Aren’t Enough: Building Sustainable Enterprise Data Quality

Towards AI

2020) Scaling Laws for Neural Language Models [link] First formal study documenting empirical scaling laws Published by OpenAI The Data Quality Conundrum Not all data is created equal. AI model training requires extensive computational resources, with companies investing billions in AI clusters.

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Intuitive robotic manipulator control with a Myo armband

Mlearning.ai

It turned out that a better solution was to annotate data by using a clustering algorithm, in particular, I chose the popular K-means. While SVM is a supervised machine learning classifier, this one belongs to the family of unsupervised learning algorithms. Machine learning would be a lot easier otherwise.

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NASA ML Lead on its WorldView citizen scientist no-code tool

Snorkel AI

And that’s the power of self-supervised learning. But desert, ocean, desert, in this way, I think that’s what the power of self-supervised learning is. It’s essentially self -supervised learning. This is the example from California from 2020. So here’s this example.

ML 52
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NASA ML Lead on its WorldView citizen scientist no-code tool

Snorkel AI

And that’s the power of self-supervised learning. But desert, ocean, desert, in this way, I think that’s what the power of self-supervised learning is. It’s essentially self -supervised learning. This is the example from California from 2020. So here’s this example.

ML 52