Marktechpost

JULY 17, 2023

The system analyzes visual data before categorizing an object in a photo or video under a predetermined heading. One of the most straightforward computer vision tools, TensorFlow, enables users to create machine learning models for computer vision-related tasks like facial recognition, picture categorization, object identification, and more.

Computer Vision 95

Computer Vision Neural Network Machine Learning Deep Learning 95

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

PyImageSearch

NOVEMBER 13, 2023

For example, image classification, image search engines (also known as content-based image retrieval, or CBIR), simultaneous localization and mapping (SLAM), and image segmentation, to name a few, have all been changed since the latest resurgence in neural networks and deep learning. 2015 ; Redmon and Farhad, 2016 ), and others.

Convolutional Neural Networks 117

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 117

Viso.ai

DECEMBER 3, 2023

Hence, rapid development in deep convolutional neural networks (CNN) and GPU’s enhanced computing power are the main drivers behind the great advancement of computer vision based object detection. Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

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

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

Explosion

NOVEMBER 9, 2016

Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. Most neural network models begin by tokenising the text into words, and embedding the words into vectors. 2016) introduce an attention mechanism that takes a single matrix and outputs a single vector.

Deep Learning 52

Deep Learning NLP Convolutional Neural Networks Neural Network 52