NYU Center for Data Science

JULY 18, 2023

Their work specializes in signal processing and inverse problems, machine learning and deep learning, and high-dimensional statistics and probability. The group works on machine learning in a broad range of applications, predominately in computer perception, natural language understanding, robotics, and healthcare.

Data Science 98

Data Science Machine Learning Machine Learning Natural Language Processing 98

AWS Machine Learning Blog

DECEMBER 12, 2023

In this post, we’ll summarize training procedure of GPT NeoX on AWS Trainium , a purpose-built machine learning (ML) accelerator optimized for deep learning training. In this post, we showed cost-efficient training of LLMs on AWS deep learning hardware. We’ll outline how we cost-effectively (3.2

AWS 132

AWS Machine Learning Machine Learning Deep Learning 132

AWS Machine Learning Blog

APRIL 18, 2023

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. Defining hyperparameters involves setting the values for various parameters used during the training process of an ML model.

ML 96

ML ML Deep Learning Deep Learning 96

Towards AI

APRIL 19, 2024

Intelligent Medical Objects 👉Industry domain: AI, Health Tech, IT, NLP, Software, Analytics, Generative AI 👉Location: 3 offices 👉Year founded: 1994 👉Programming languages deployed: Angular, C#, SQL, Scikit, TensorFlow, Spark, GitHub, R, Python 👉Benefits: Flexible time off, family medical leave, pet insurance, (..)

AI 105

AI AI Artificial Intelligence Artificial Intelligence 105

AWS Machine Learning Blog

JANUARY 30, 2023

Amazon SageMaker provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning.

Algorithm 108

Algorithm Clustering Machine Learning Machine Learning 108

AWS Machine Learning Blog

APRIL 18, 2023

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. Defining hyperparameters involves setting the values for various parameters used during the training process of an ML model.

ML 52

ML ML Deep Learning Deep Learning 52

DagsHub

DECEMBER 23, 2024

  Overview of the types of active learning | Source : Settles, B. Active Learning Literature Survey Pool-Based Active Learning Overview Pool-based active learning is the most commonly used approach in practical applications.   Traditional Active Learning has the following characteristics.

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Clustering 52