ODSC - Open Data Science

JULY 3, 2025

In the AI field, Ivan has been building web app integrations with JavaScript SDKs and researching automated workflows using AI models since 2023. Suman Debnath, Principal AI/ML Advocate at Amazon Web Services Suman Debnath is a Principal Machine Learning Advocate at Amazon Web Services.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

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

BAIR

APRIL 6, 2023

This approach is known as “Fleet Learning,” a term popularized by Elon Musk in 2016 press releases about Tesla Autopilot and used in press communications by Toyota Research Institute , Wayve AI , and others. Furthermore, due to advances in cloud robotics , the fleet can offload data, memory, and computation (e.g.,

Algorithm 98

Algorithm Artificial Intelligence Artificial Intelligence Supervised Learning 98

Google Research AI blog

JANUARY 18, 2023

With this post, I am kicking off a series in which researchers across Google will highlight some exciting progress we've made in 2022 and present our vision for 2023 and beyond. I will begin with a discussion of language, computer vision, multi-modal models, and generative machine learning models.

ML 132

ML ML AI AI 132

Mlearning.ai

SEPTEMBER 4, 2023

2023) [15] The term “Chinchilla optimal” refers to having a set number of FLOPS (floating point operations per second) or a fixed compute budget and asks what the most suitable model and data size is to minimize loss or optimize accuracy. International Conference on Learning Representations. [20] 12] Figure 1: Rishi Bommasani et al. “Do

AI 52

AI AI Azure Machine Learning 52

DrivenData Labs

FEBRUARY 3, 2025

I generated unlabeled data for semi-supervised learning with Deberta-v3, then the Deberta-v3-large model was used to predict soft labels for the unlabeled data. The semi-supervised learning was repeated using the gemma2-9b model as the soft labeling model. What motivated you to compete in this challenge?

Machine Learning 130

Machine Learning Machine Learning Data Science Natural Language Processing 130