Snorkel AI

MARCH 1, 2023

Model architectures that qualify as “supervised learning”—from traditional regression models to random forests to most neural networks—require labeled data for training. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. What are some examples of Foundation Models?

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Bugra Akyildiz

NOVEMBER 17, 2024

They have shown impressive performance in various computer vision tasks, often outperforming traditional convolutional neural networks (CNNs). Airbnb uses ViTs for several purposes in their photo tour feature: Image classification : Categorizing photos into different room types (bedroom, bathroom, kitchen, etc.) or amenities.

Convolutional Neural Networks 52

Convolutional Neural Networks Metadata Computer Vision Python 52

Viso.ai

DECEMBER 18, 2023

YOLO’s architecture was a significant revolution in the real-time object detection space, surpassing its predecessor – the Region-based Convolutional Neural Network (R-CNN). The backbone is a pre-trained Convolutional Neural Network (CNN) that extracts low, medium, and high-level feature maps from an input image.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Algorithm 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

In this example figure, features are extracted from raw historical data, which are then are fed into a neural network (NN). Parallel computing Parallel computing refers to carrying out multiple processes simultaneously, and can be categorized according to the granularity at which parallelism is supported by the hardware.

ML 113

ML Deep Learning Algorithm Large Language Models 113

Explosion

AUGUST 1, 2019

Deep neural networks have offered a solution, by building dense representations that transfer well between tasks. In the last few years, research has shown that linguistic knowledge can be acquired effectively from unlabelled text, so long as the network is large enough to represent the long-tail of rare usage phenomena.

BERT 52

BERT NLP Neural Network Categorization 52

Explosion

FEBRUARY 12, 2017

A neural bag-of-words model for text-pair classification When designing a neural network for a text-pair task, probably the most important decision is whether you want to represent the meanings of the texts independently , or jointly. Most NLP neural networks start with an embedding layer.

Neural Network 40

Neural Network Deep Learning Natural Language Processing NLP 40

Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). The last Dense layer is the network’s output layer, it takes in a variable shape which can be either 4 or 3, denoting the number of classes for therapist and client. Uysal and Gunal, 2014). Manning C. and Schutze H., Cambridge: MIT Press.

BERT 52

BERT NLP Natural Language Processing Algorithm 52

Probably Approximately a Scientific Blog

JANUARY 12, 2021

The release of Google Translate’s neural models in 2016 reported large performance improvements: “60% reduction in translation errors on several popular language pairs”. With that said, the path to machine commonsense is unlikely to be brute force training larger neural networks with deeper layers.

Natural Language Processing 52

Natural Language Processing BERT NLP Neural Network 52