AWS Machine Learning Blog

OCTOBER 30, 2024

For this demonstration, we use a public Amazon product dataset called Amazon Product Dataset 2020 from a kaggle competition. XGBoost classification XGBoost (Extreme Gradient Boosting) classification is a machine learning technique used for classification tasks. Kara is passionate about innovation and continuous learning.

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DrivenData Labs

JUNE 14, 2023

Self-supervision: As in the Image Similarity Challenge , all winning solutions used self-supervised learning and image augmentation (or models trained using these techniques) as the backbone of their solutions. His research interest is deep metric learning and computer vision.

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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.

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Dataconomy

AUGUST 22, 2024

Aleksandr Timashov is an ML Engineer with over a decade of experience in AI and Machine Learning. Please tell our readers about your background and how you got into Data Science and Machine Learning? The transition to Machine Learning felt natural given my mathematical background.

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AssemblyAI

AUGUST 22, 2023

Using such data to train a model is called “supervised learning” On the other hand, pretraining requires no such human-labeled data. This process is called “self-supervised learning”, and is identical to supervised learning except for the fact that humans don’t have to create the labels.

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Applied Data Science

MARCH 11, 2022

In contrast to classification, a supervised learning paradigm, generation is most often done in an unsupervised manner: for example an autoencoder , in the form of a neural network, can capture the statistical properties of a dataset. One does not need to look into the math to see that it’s inherently more difficult.

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Snorkel AI

MARCH 1, 2023

Foundation Models (FMs), such as GPT-3 and Stable Diffusion, mark the beginning of a new era in machine learning and artificial intelligence. Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. What is self-supervised learning?

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Towards AI

MARCH 28, 2023

Key Takeaways Problem Statement NASA Harvest Field Boundary Detection challenge is a machine learning challenge organized by Zindi.Africa in collaboration with NASA Harvest situated at the University of Maryland, and Radiant Earth Foundation. And that notwithstanding, I set out to learn all that I could find pertaining to this task.

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Mlearning.ai

APRIL 16, 2023

It may seem simple linear regression is neglected in the machine learning world of today. In this tutorial, you will learn about the concepts behind simple linear regression. There are various types of regressions used in data science and machine learning. 2021) Machine Learning for Beginners.

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Snorkel AI

FEBRUARY 6, 2023

  Anirudh Koul is Machine Learning Lead for the NASA Frontier Development Lab and the Head of Machine Learning Sciences at Pinterest. 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.

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Snorkel AI

FEBRUARY 6, 2023

Anirudh Koul is Machine Learning Lead for the NASA Frontier Development Lab and the Head of Machine Learning Sciences at Pinterest. 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.

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Towards AI

FEBRUARY 11, 2025

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. Some argue that while scaling has driven progress so far, we may eventually exhaust high-quality training data, leading to diminishing returns.

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Mlearning.ai

JANUARY 31, 2023

Machine learning is a popular choice here. I tried several other machine learning classifiers, but SVM turned out to be the best. Furthermore, it involves just dot-products, a fast operation for nowadays machines to carry on. Of course, any machine learning algorithm requires a proper dataset to train on.

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Google Research AI blog

JANUARY 18, 2023

I will begin with a discussion of language, computer vision, multi-modal models, and generative machine learning models. Language Models The progress on larger and more powerful language models has been one of the most exciting areas of machine learning (ML) research over the last decade.

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Snorkel AI

MAY 25, 2023

Data scientists and researchers train LLMs on enormous amounts of unstructured data through self-supervised learning. The model then predicts the missing words (see “what is self-supervised learning?” Next, OpenAI released GPT-3 in June of 2020. But there are several ways to coax what you need from them.

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Snorkel AI

MAY 25, 2023

Data scientists and researchers train LLMs on enormous amounts of unstructured data through self-supervised learning. The model then predicts the missing words (see “what is self-supervised learning?” Next, OpenAI released GPT-3 in June of 2020. But there are several ways to coax what you need from them.

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Natural Language Processing Python Machine Learning Machine Learning 52

Mlearning.ai

SEPTEMBER 4, 2023

2020) and Chinchilla scaling laws with prior large language model and BloombergGPT parameter and data sizes. Tie-ups between model makers and cloud companies include OpenAI with Microsoft Azure, Anthropic and Cohere with Google Cloud, and the machine-learning startup Hugging Face with Amazon Web Services. 32] Alex Wang, et al.

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Snorkel AI

JANUARY 16, 2024

We have best practices in academic machine learning around having held-out test sets and validation sets. As humans, we learn a lot of general stuff through self-supervised learning by just experiencing the world. DK: Absolutely. This is one big issue I have with prompt engineering these days. Everybody knows that.

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Snorkel AI

JANUARY 16, 2024

We have best practices in academic machine learning around having held-out test sets and validation sets. As humans, we learn a lot of general stuff through self-supervised learning by just experiencing the world. DK: Absolutely. This is one big issue I have with prompt engineering these days. Everybody knows that.

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Supervised Learning AI AI Algorithm 52

Data Science Blog

MARCH 14, 2023

Von Data Science spricht auf Konferenzen heute kaum noch jemand und wurde hype-technisch komplett durch Machine Learning bzw. GPT-3 ist jedoch noch komplizierter, basiert nicht nur auf Supervised Deep Learning , sondern auch auf Reinforcement Learning. Oktober 2014 ↑ Bussler, Frederik (July 21, 2020).

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IBM Journey to AI blog

JUNE 23, 2023

The introduction of ChatGPT capabilities has generated a lot of interest in generative AI foundation models (these are pre-trained on unlabeled datasets and leverage self-supervised learning with the help of Large Language Models using a neural network ). The ROE ranges also varied by country, from –5% to +13% [1].

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Kaggle

JULY 29, 2020

Photo by Clay Banks on Unsplash Let’s learn about David! link] David Mezzetti is the founder of NeuML, a data analytics and machine learning company that develops innovative products backed by machine learning. I was looking forward to the 2020 tournament and had a model I was very excited about.

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ETL Data Scientist Data Science Machine Learning 71