2015 and Supervised Learning - Data Science Current

Counting shots, making strides: Zero, one and few-shot learning unleashed

Data Science Dojo

DECEMBER 8, 2023

Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.

Supervised Learning

Supervised Learning Machine Learning Machine Learning Artificial Intelligence

Data Science Dojo - Untitled Article

Data Science Dojo

DECEMBER 8, 2023

Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.

Supervised Learning

Supervised Learning Machine Learning Machine Learning Artificial Intelligence

Big Data – Das Versprechen wurde eingelöst

Data Science Blog

MARCH 14, 2023

Die vollautomatisierte Analyse von textlicher Sprache, von Fotos oder Videomaterial war 2015 noch Nische, gehört heute jedoch zum Alltag hinzu. Während 2015 noch von neuen Geschäftsmodellen mit Big Data geträumt wurde, sind Data as a Service und AI as a Service heute längst Realität! ChatGPT basiert auf GPT-3.5

Big Data

Big Data Big Data Apache Hadoop Data Science

Webinars

How to Achieve High-Accuracy Results When Using LLMs

MORE WEBINARS

Modern NLP: A Detailed Overview. Part 2: GPTs

Towards AI

JULY 23, 2023

Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train. In 2015, Andrew M. As supervised learning required huge datasets for image processing to train super-deep models, the scarcity of data became an issue.

Natural Language Processing

Natural Language Processing Supervised Learning Deep Learning Deep Learning

Meet the Winners of the Youth Mental Health Narratives Challenge

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.

Machine Learning

Machine Learning Machine Learning Data Science Natural Language Processing

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

AssemblyAI

JULY 18, 2023

We have begun to observe diminishing returns and are already exploring other promising research directions into multimodality and self-supervised learning. While this progress has been exciting, bootstrapping strong teacher models was bound to run into an asymptotic limit and stop bearing fruit. Panayotov, G. Povey and S.

Clustering

Clustering Supervised Learning AI AI

Getir end-to-end workforce management: Amazon Forecast and AWS Step Functions

AWS Machine Learning Blog

DECEMBER 7, 2023

Getir was founded in 2015 and operates in Turkey, the UK, the Netherlands, Germany, and the United States. Given the availability of diverse data sources at this juncture, employing the CNN-QR algorithm facilitated the integration of various features, operating within a supervised learning framework.

AWS

AWS Algorithm Data Science Machine Learning

Foundation models: a guide

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

Natural Language Processing Supervised Learning Machine Learning Machine Learning

MLOps and the evolution of data science

IBM Journey to AI blog

AUGUST 11, 2023

The term was originally coined in 2015 in a published research paper called, “Hidden Technical Debts in the Machine Learning System,” which highlighted common problems that arose when using machine learning for business applications. Foundation models aim to solve this problem.

Data Science

Data Science Machine Learning Machine Learning ML

How ChatGPT really works and will it change the field of IT and AI??—?a deep dive

Chatbots Life

MAY 12, 2023

Such models can also learn from a set of few examples The process of presenting a few examples is also called In-Context Learning , and it has been demonstrated that the process behaves similarly to supervised learning. In this model, the authors used explicit unified prompts such as “summarize:” to train the model.

AI

AI AI ML ML

LLM distillation demystified: a complete guide

Snorkel AI

FEBRUARY 13, 2024

Data science teams that want to further reduce the need for human labeling can employ supervised or semi-supervised learning methods to relabel likely-incorrect records based on the patterns set by the high-confidence data points.

Data Scientist

Data Scientist Data Science AI AI

LLM distillation demystified: a complete guide

Snorkel AI

FEBRUARY 13, 2024

Data science teams that want to further reduce the need for human labeling can employ supervised or semi-supervised learning methods to relabel likely-incorrect records based on the patterns set by the high-confidence data points.

Data Scientist

Data Scientist Data Science AI AI

SyntaxNet in context: Understanding Google's new TensorFlow NLP model

Explosion

MAY 12, 2016

For instance, we parsed every comment posted to Reddit in 2015, and used word2vec on the phrases, entities and words. If you’re populating a knowledge base, you can extend the state representation to include your target semantics, and learn it jointly with the syntax.

Natural Language Processing

Natural Language Processing Supervised Learning Algorithm

Google Research, 2022 & Beyond: Language, Vision and Generative Models

Google Research AI blog

JANUARY 18, 2023

Diffusion models, introduced in “ Deep Unsupervised Learning using Nonequilibrium Thermodynamics ” in 2015, systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. Goodfellow, et al. Middle: From M. Lucic, et al. Right: From Imagen.

ML

ML ML AI AI

Backpropagation

Dataconomy

MARCH 20, 2025

Characteristics of backpropagation Backpropagation primarily operates under the framework of supervised learning, where the model is trained on labeled data. This allows the system to understand the expected outcomes for various inputs, facilitating the learning process.

Predictive Analytics

Predictive Analytics Machine Learning Machine Learning Deep Learning

Data Science Current

Counting shots, making strides: Zero, one and few-shot learning unleashed

Data Science Dojo - Untitled Article

Webinars

Trending Sources

Big Data – Das Versprechen wurde eingelöst

Webinars

Modern NLP: A Detailed Overview. Part 2: GPTs

Meet the Winners of the Youth Mental Health Narratives Challenge

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

Getir end-to-end workforce management: Amazon Forecast and AWS Step Functions

Foundation models: a guide

MLOps and the evolution of data science

How ChatGPT really works and will it change the field of IT and AI??—?a deep dive

LLM distillation demystified: a complete guide

LLM distillation demystified: a complete guide

SyntaxNet in context: Understanding Google's new TensorFlow NLP model

Google Research, 2022 & Beyond: Language, Vision and Generative Models

Backpropagation

Stay Connected