ODSC - Open Data Science

MARCH 22, 2023

Be sure to check out his session, “ Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI ,” there! Anybody who has worked on a real-world ML project knows how messy data can be. Everybody knows you need to clean your data to get good ML performance. A common gripe I hear is: “Garbage in, garbage out.

ML 88

ML ML Data Scientist AI 88

Explosion

MAY 17, 2023

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.

Supervised Learning 135

Supervised Learning Natural Language Processing Clustering Machine Learning 135

AWS Machine Learning Blog

AUGUST 16, 2023

Training machine learning (ML) models to interpret this data, however, is bottlenecked by costly and time-consuming human annotation efforts. One way to overcome this challenge is through self-supervised learning (SSL). Machine Learning Engineer at AWS. The following are a few example RGB images and their labels.

ML 81

ML ML Data Scientist AWS 81

How to Learn Machine Learning

OCTOBER 10, 2024

The quality of your training data in Machine Learning (ML) can make or break your entire project. Real-Life Examples of Poor Training Data in Machine Learning Amazon’s Hiring Algorithm Disaster In 2018, Amazon made headlines for developing an AI-powered hiring tool to screen job applicants. Sounds great, right?

Machine Learning 52

Machine Learning Machine Learning Data Quality Algorithm 52

Google Research AI blog

JANUARY 18, 2023

Language Models Computer Vision Multimodal Models Generative Models Responsible AI* Algorithms ML & Computer Systems Robotics Health General Science & Quantum Community Engagement * Other articles in the series will be linked as they are released. language models, image classification models, or speech recognition models).

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ML ML AI AI 132

Snorkel AI

MARCH 1, 2023

Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. What is self-supervised learning? Self-supervised learning is a kind of machine learning that creates labels directly from the input data. Find out in the guide below.

Natural Language Processing 83

Natural Language Processing Supervised Learning Machine Learning Machine Learning 83

Mlearning.ai

JULY 31, 2023

One example is the Pairwise Inner Product (PIP) loss, a metric designed to measure the dissimilarity between embeddings using their unitary invariance (Yin and Shen, 2018). Yin and Shen (2018) accompany their research with a code implementation on GitHub here. Fortunately, there is; use an embedding loss. Equation 2.3.1. and Auli, M.,

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Algorithm 52