ODSC - Open Data Science

MARCH 22, 2023

Previously, he was a senior scientist at Amazon Web Services developing AutoML and Deep Learning algorithms that now power ML applications at hundreds of companies. A recent report by Cloudfactory found that human annotators have an error rate between 7–80% when labeling data (depending on task difficulty and how much annotators are paid).

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ML ML Data Scientist AI 88

IBM Journey to AI blog

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. They can also perform self-supervised learning to generalize and apply their knowledge to new tasks. An open-source model, Google created BERT in 2018.

AI 70

AI AI Machine Learning Machine Learning 70

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?

Natural Language Processing 83

Natural Language Processing Supervised Learning Machine Learning Machine Learning 83

Towards AI

JANUARY 7, 2025

During this process, they learn language patterns but typically are not capable of following instructions or answering questions. In the case of GPT models, this self-supervised learning includes predicting the next word (unidirectional) based on their training data, which is often webpages.

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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. Andrew Ang is a Sr.

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ML ML Data Scientist AWS 94

AWS Machine Learning Blog

AUGUST 7, 2023

The transformer architecture was the foundation for two of the most well-known and popular LLMs in use today, the Bidirectional Encoder Representations from Transformers (BERT) 4 (Radford, 2018) and the Generative Pretrained Transformer (GPT) 5 (Devlin 2018).

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AWS ML ML Data Science 96

Explosion

JANUARY 12, 2018

While we wouldn’t say bootstrapping is for everyone, it’s been a joy to build the company that we want to build, the way we want to build it: Worked with some amazing companies and shipped custom, cutting-edge machine learning solutions for a range of exciting problems. spaCy’s Machine Learning library for NLP in Python.

Machine Learning 52

Machine Learning Machine Learning Supervised Learning Python 52

Pickl AI

APRIL 10, 2023

As per the recent report by Nasscom and Zynga, the number of data science jobs in India is set to grow from 2,720 in 2018 to 16,500 by 2025. Top 5 Colleges to Learn Data Science (Online Platforms) 1. It offers an immersive learning experience that is a blend of conceptual and practical expertise. offers a host of courses.

Data Science 52

Data Science Machine Learning Machine Learning Python 52

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

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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ML ML AI AI 132

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?” From 2018 to the modern day, NLP researchers have engaged in a steady march toward ever-larger models.

Natural Language Processing 52

Natural Language Processing Python Machine Learning Machine Learning 52

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?” From 2018 to the modern day, NLP researchers have engaged in a steady march toward ever-larger models.

Natural Language Processing 52

Natural Language Processing Python Machine Learning Machine Learning 52

Data Science Blog

MARCH 14, 2023

Dann etwa im Jahr 2018 flachte der Hype um Big Data wieder ab, die Euphorie änderte sich in eine Ernüchterung, zumindest für den deutschen Mittelstand. Von Data Science spricht auf Konferenzen heute kaum noch jemand und wurde hype-technisch komplett durch Machine Learning bzw. Artificial Intelligence (AI) ersetzt.

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Big Data Big Data Apache Hadoop Data Science 147

DrivenData Labs

FEBRUARY 3, 2025

Recently, I became interested in machine learning, so I was enrolled in the Yandex School of Data Analysis and Computer Science Center. Machine learning is my passion and I often participate in competitions. The semi-supervised learning was repeated using the gemma2-9b model as the soft labeling model.

Machine Learning 130

Machine Learning Machine Learning Data Science Natural Language Processing 130

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].

Supervised Learning 71

Supervised Learning AI AI Artificial Intelligence 71

Dataconomy

MARCH 5, 2025

Technology and methodology DeepMind’s approach revolves around sophisticated machine learning methods that enable AI to interact with its environment and learn from experience. AlphaGo Zero: This iteration used unsupervised reinforcement learning, allowing the program to exceed its predecessors consistently.

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