ODSC - Open Data Science

APRIL 28, 2023

In the first example, we’ll be defining an architecture based on a Convolutional Neural Network (CNN) The dataset We’ll be using the same dataset as last time; a collection of 50k reviews from IMDB which are labeled as either positive or negative. components: This section details the components we specified in the nlp section.

Deep Learning 98

Deep Learning Convolutional Neural Networks Neural Network NLP 98

Mlearning.ai

APRIL 8, 2023

With the rise of deep learning (deep learning means multiple levels of neural networks) and neural networks, models such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) began to be used in NLP.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

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

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). GANs based Models GANs were first introduced in 2014 and have been modified for use in various applications, style transfer being one of them. What is Perceptual Loss?

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

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.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision AI AI 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

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%

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Convolutional Neural Networks Neural Network 52

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

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

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. The synthesis network is composed of convolutional layers that progressively refine the image from a low resolution to the final high resolution.

Explainability 52

Explainability Computer Vision Convolutional Neural Networks Neural Network 52

Lexalytics

APRIL 5, 2021

A paper that exemplifies the Classifier Cage Match era is LeCun et al [ 109 ], which pits support vector machines (SVMs), k-nearest neighbor (KNN) classifiers, and convolution neural networks (CNNs) against each other to recognize images from the NORB database. 90,575 trainable parameters, placing it in the small-feature regime.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

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

Natural Language Processing Deep Learning NLP Convolutional Neural Networks 40

Viso.ai

DECEMBER 3, 2023

Today, the most powerful image processing models are based on convolutional neural networks (CNNs). With the latest advances in hardware and model efficiency, new applications with real-time requirements are becoming possible and economically feasible.

Deep Learning 98

Deep Learning Computer Vision Neural Network Robotics 98

deepsense.ai

MAY 7, 2023

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. The first network is called a Proposal Network – it parses the image and selects several bounding boxes that surround an object of interest: a face, in our case.

Neural Network 52

Neural Network Convolutional Neural Networks Explainability Deep Learning 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. Doc2Vec: introduced in 2014, adds on to the Word2Vec model by introducing another ‘paragraph vector’. using its Spectrogram ).

Machine Learning 52

Machine Learning BERT Neural Network OpenAI 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. Generative adversarial networks , developed in 2014, set up two models working against each other. natural images). Left: From I.

Computer Vision 132

Computer Vision Auto-classification Large Language Models Neural Network 132

Marek Rei

NOVEMBER 12, 2019

Evaluations on CoNLL 2014 and JFLEG show a considerable improvement over previous best results of neural models, making this work comparable to state-of-the art on error correction. Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks Awni Y. Tison, Codie Bourn, Mintu P. Turakhia, Andrew Y.

Machine Learning 52

Machine Learning NLP Neural Network BERT 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

ML Review

MAY 3, 2018

22] On a high level in the architecture, the frames extracted from a video sequence are processed in small sets within a Convolutional Neural Network (CNN), [23] while an LSTM-variant runs on the CNN output sequentially to generate output characters. An Intuitive Explanation of Convolutional Neural Networks.

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52

Marek Rei

JANUARY 17, 2018

Neural activity by brain region, from Wehbe et al. Variational Neural Machine Translation Biao Zhang, Deyi Xiong, Jinsong Su, Hong Duan, Min Zhang. link] They start with the neural machine translation model using alignment, by Bahdanau et al. 2014), and add an extra variational component. 2014), used in this work.

Machine Learning 52

Machine Learning NLP Neural Network Convolutional Neural Networks 52

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

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). Sequence to sequence learning with neural networks.

NLP 52

NLP Neural Network Deep Learning Natural Language Processing 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.

Neural Network 263

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