ODSC - Open Data Science

APRIL 28, 2023

Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e. Convolutional Neural Network for sentiment analysis A CNN model is a type of neural architecture that is based on learned matrices of numbers (filters) that slide (convolve) over the input data.

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Viso.ai

MARCH 12, 2024

Today’s boom in computer vision (CV) started at the beginning of the 21 st century with the breakthrough of deep learning models and convolutional neural networks (CNN). The same CNN, with an extra sixth convolutional layer, was used to classify the entire ImageNet Fall 2011 release (15M images, 22K categories).

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Deep Learning 52

Viso.ai

MAY 7, 2024

GoogLeNet, released in 2014, set a new benchmark in object classification and detection through its innovative approach (achieving a top-5 error rate of 6.7%, nearly half the error rate of the previous year’s winner ZFNet with 11.7%) in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). In the original paper, it is set to 0.3.

Explainability 52

Explainability Neural Network Convolutional Neural Networks Deep Learning 52

Viso.ai

DECEMBER 3, 2023

Today, the most powerful image processing models are based on convolutional neural networks (CNNs). A popular library that uses neural networks for real-time human pose estimation in 3D, even for multi-person use cases, is named OpenPose. High-Resolution Net (HRNet) is a neural network for human pose estimation.

Deep Learning 98

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ML Review

JUNE 4, 2018

Image captioning (circa 2014) Image captioning research has been around for a number of years, but the efficacy of techniques was limited, and they generally weren’t robust enough to handle the real world. However, in 2014 a number of high-profile AI labs began to release new approaches leveraging deep learning to improve performance.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

Lexalytics

APRIL 5, 2021

This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas. (I Around this time a new graduate student, Geoffrey Hinton, decided that he would study the now discredited field of neural networks.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Viso.ai

DECEMBER 6, 2024

Autonomous Driving applying Semantic Segmentation in autonomous vehicles Semantic segmentation is now more accurate and efficient thanks to deep learning techniques that utilize neural network models. Levels of Automation in Vehicles – Source Here we present the development timeline of the autonomous vehicles.

Computer Vision 52

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Viso.ai

JUNE 19, 2024

However, when deep learning became popular in the 2010s, DeepPose was introduced by researchers at Facebook in 2014, this was an inspirational model that utilized Convolutional Neural Networks ( CNNs ) to effectively detect human poses directly from images. one for the left elbow, one for the right knee, etc.).

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 52

Dlabs.ai

NOVEMBER 16, 2020

Suguard is an internal project we’ve been developing since 2014, which is when we founded DiabetesLab : our second company focused on creating advanced software that helps people manage an illness using AI. The technology uses convolutional neural networks to indicate likely issues on a patient’s retina, boasting accuracy levels of 92.3%

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Viso.ai

JULY 17, 2024

An image can be represented by the relationships between the activations of features detected by a convolutional neural network (CNN). Previous methods directly fed the semantic layout as input to the deep neural network, which then the model processed through stacks of convolution, normalization, and nonlinearity layers.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Google Research AI blog

JANUARY 18, 2023

One trend that started with our work on Vision Transformers in 2020 is to use the Transformer architecture in computer vision models rather than convolutional neural networks. The neural network perceives an image, and generates a sequence of tokens for each object, which correspond to bounding boxes and class labels.

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Mlearning.ai

JUNE 16, 2023

Practitioners first trained a Convolutional Neural Network (CNN) to perform image classification on ImageNet (i.e. OverFeat: Integrated Recognition, Localization and Detection Using Convolutional Networks.” February 23, 2014. pre-training). fine-tuning). January 29, 2015.

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Convolutional Neural Networks Computer Vision AI AI 52

Viso.ai

DECEMBER 6, 2023

State of Computer Vision Tasks in 2024 The field of computer vision today involves advanced AI algorithms and architectures, such as convolutional neural networks (CNNs) and vision transformers ( ViTs ), to process, analyze, and extract relevant patterns from visual data. Get a demo here.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Categorization 52

Viso.ai

MAY 31, 2024

Deep Learning Approaches Convolutional Neural Networks (CNNs) : The CNNs including AlexNet , VGGNet , and ResNet helped solve computer vision problems by learning the hierarchal features directly from the Pascal VOC data. These models were able to set benchmark accuracy on the Pascal VOC classification and detection challenges.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 52

Viso.ai

JULY 9, 2024

StyleGAN is GAN (Generative Adversarial Network), a Deep Learning (DL) model, that has been around for some time, developed by a team of researchers including Ian Goodfellow in 2014. These two networks compete against each other in a zero-sum game.

Explainability 52

Explainability Computer Vision Convolutional Neural Networks Neural Network 52

Mlearning.ai

JUNE 8, 2023

Images can be embedded using models such as convolutional neural networks (CNNs) , Examples of CNNs include VGG , and Inception. The Unreasonable Effectiveness Of Neural Network Embeddings An embedding is a low-dimensional vector representation that captures relationships in higher dimensional input data.

Machine Learning 52

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John Snow Labs

MAY 23, 2023

The first VQA dataset was DAQUAR, released in 2014. VQA frameworks combine two Deep Learning architectures to deliver the final answer: Convolutional Neural Networks (CNN) for image recognition and Recurrent Neural Network (RNN) (and its special variant Long Short Term Memory networks or LSTM) for NLP processing.

Natural Language Processing 40

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deepsense.ai

MAY 7, 2023

Next, we embed the images using an Inception-based [ 5 ] neural network. This solution is based on several Convolutional Neural Networks that work in a cascade fashion to locate the face with some landmarks in an image. 2022 [link] Going deeper with convolutions , Szegedy et al.

Neural Network 52

Neural Network Convolutional Neural Networks Explainability Deep Learning 52

Marek Rei

NOVEMBER 12, 2019

Unsupervised Recurrent Neural Network Grammars Yoon Kim, Alexander Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, Gábor Melis. link] Extending recurrent neural network grammars to the unsupervised setting, discovering constituency parses only from plain text. Harvard, Oxford, DeepMind. NAACL 2019. Turakhia, Andrew Y.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

ML Review

MAY 3, 2018

Similar to the advancements seen in Computer Vision, NLP as a field has seen a comparable influx and adoption of deep learning techniques, especially with the development of techniques such as Word Embeddings [6] and Recurrent Neural Networks (RNNs) [7]. Neural network-based approaches are typically characterised by heavy data demands.

Neural Network 52

Neural Network Computer Vision Deep Learning NLP 52

Heartbeat

APRIL 18, 2023

The VGG model The VGG ( Visual Geometry Group ) model is a deep convolutional neural network architecture for image recognition tasks. It was introduced in 2014 by a group of researchers (A. Zisserman and K. Simonyan) from the University of Oxford.

Convolutional Neural Networks 52

Convolutional Neural Networks Deep Learning Neural Network Machine Learning 52

Marek Rei

JANUARY 17, 2018

They present a simple classifier that achieves unexpectedly good results, and a neural network based on attention that beats all previous results by quite a margin. Neural activity by brain region, from Wehbe et al. Variational Neural Machine Translation Biao Zhang, Deyi Xiong, Jinsong Su, Hong Duan, Min Zhang.

Machine Learning 40

Machine Learning NLP Neural Network Convolutional Neural Networks 40

NLP People

JULY 24, 2019

Neural Networks are the workhorse of Deep Learning (cf. Convolutional Neural Networks have seen an increase in the past years, whereas the popularity of the traditional Recurrent Neural Network (RNN) is dropping. White (2014). Neural Network Methods in Natural Language Processing.

NLP 52

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Viso.ai

DECEMBER 21, 2023

From the development of sophisticated object detection algorithms to the rise of convolutional neural networks (CNNs) for image classification to innovations in facial recognition technology, applications of computer vision are transforming entire industries. Thus, positioning him as one of the top AI influencers in the world.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Unite.AI

JUNE 25, 2024

These techniques involve training neural networks on large datasets of images and videos, enabling them to generate synthetic media that closely mimics real-life appearances and movements. The advent of GANs in 2014 marked a significant milestone, allowing the creation of more sophisticated and hyperrealistic deepfakes.

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