Viso.ai

NOVEMBER 5, 2024

The YOLO Family of Models The first YOLO model was introduced back in 2016 by a team of researchers, marking a significant advancement in object detection technology. Convolution Layer: The concatenated feature descriptor is then passed through a Convolution Neural Network.

Convolutional Neural Networks 98

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AssemblyAI

NOVEMBER 8, 2023

  Each stage leverages a deep neural network that operates as a sequence labeling problem but at different granularities: the first network operates at the token level and the second at the character level. Training Data : We trained this neural network on a total of 3.7 billion words). billion words.

Neural Network 147

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Viso.ai

DECEMBER 3, 2023

The recent deep learning algorithms provide robust person detection results. However, deep learning models such as YOLO that are trained for person detection on a frontal view data set still provide good results when applied for overhead view person counting ( TPR of 95%, FPR up to 0.2% ).

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

Unite.AI

MARCH 5, 2024

Object detection has seen rapid advancement in recent years thanks to deep learning algorithms like YOLO (You Only Look Once). Review of Previous YOLO Versions The YOLO (You Only Look Once) family of models has been at the forefront of fast object detection since the original version was published in 2016.

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Viso.ai

DECEMBER 4, 2023

Artificial Neural Networks (ANNs) have been demonstrated to be state-of-the-art in many cases of supervised learning, but programming an ANN manually can be a challenging task. As a result, frameworks such as TensorFlow and PyTorch have been created to simplify the creation, serving, and scaling of deep learning models.

Neural Network 98

Neural Network Deep Learning Python Machine Learning 98

PyImageSearch

NOVEMBER 13, 2023

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al.

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Marktechpost

NOVEMBER 18, 2023

In Australia’s Great Barrier Reef in 2016, bleaching killed 29–50% of the coral. Researchers from Chosun University in this project aim to create deep learning and handmade feature extraction methods that can withstand the geometric and visual variances found in photos of maritime environments.

Machine Learning 113

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Viso.ai

DECEMBER 17, 2024

PaddlePaddle (PArallel Distributed Deep LEarning), is a deep learning open-source platform. It is China’s very first independent R&D deep learning platform. After that, this framework has been officially opened to professional communities since 2016. To learn more, book a demo with our team.

Deep Learning 52

Deep Learning Computer Vision Neural Network Artificial Intelligence 52

Explosion

NOVEMBER 9, 2016

Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. now features deep learning models for named entity recognition, dependency parsing, text classification and similarity prediction based on the architectures described in this post. Here’s how to do that.

Deep Learning 52

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Marktechpost

JULY 17, 2023

MATLAB Image, video, and signal processing, deep learning, machine learning, and other applications can all benefit from the programming environment MATLAB. Keras A Python-based open-source software package called Keras serves as an interface for the TensorFlow framework for machine learning.

Computer Vision 98

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Marktechpost

JULY 24, 2023

Many studies have been motivated to explore hidden hierarchical patterns in the large volume of weather datasets for weather forecasting due to the recent development of deep learning techniques, the widespread availability of massive weather observation data, and the advent of information and computer technology.

Deep Learning 52

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Mlearning.ai

JANUARY 27, 2023

Save this blog for comprehensive resources for computer vision Source: appen Working in computer vision and deep learning is fantastic because, after every few months, someone comes up with something crazy that completely changes your perspective on what is feasible. Template Matching —  Video Tutorial , Written Tutorial 12.

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Marktechpost

SEPTEMBER 1, 2023

This can be accomplished in several ways, such as by employing neural networks to create entirely unique music or utilizing machine learning algorithms to assess existing music and produce new compositions in a similar style. AIVA, built in 2016, is another outstanding AI music creator consistently attracting notice.

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Viso.ai

OCTOBER 15, 2024

However, in recent years, human pose estimation accuracy achieved great breakthroughs with Convolutional Neural Networks (CNNs). The method won the COCO 2016 Keypoints Challenge and is popular for quality and robustness in multi-person settings. Pose Estimation is still a pretty new computer vision technology.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

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

John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. For visual question answering in Deep Learning using NLP, public datasets play a crucial role.

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ML Review

MAY 3, 2018

Much the same way we iterate, link and update concepts through whatever modality of input our brain takes — multi-modal approaches in deep learning are coming to the fore. While an oversimplification, the generalisability of current deep learning approaches is impressive.

Neural Network 52

Neural Network Computer Vision Deep Learning NLP 52

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). We split them into two categories – classical CV approaches, and papers based on deep-learning. Find the SURF paper here.

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Computer Vision Convolutional Neural Networks Neural Network Deep Learning 52

ML Review

JUNE 4, 2018

However, in 2014 a number of high-profile AI labs began to release new approaches leveraging deep learning to improve performance. The first paper, to the best of our knowledge, to apply neural networks to the image captioning problem was Kiros et al. The illustration shows how a word is generated at every time step.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

Viso.ai

MAY 7, 2024

GoogLeNet’s deep learning model was deeper than all the previous models released, with 22 layers in total. Increasing the depth of the Machine Learning model is intuitive, as deeper models tend to have more learning capacity and as a result, this increases the performance of a model.

Explainability 52

Explainability Neural Network Convolutional Neural Networks Deep Learning 52

Pickl AI

AUGUST 22, 2024

Introduction Deep Learning frameworks are crucial in developing sophisticated AI models, and driving industry innovations. By understanding their unique features and capabilities, you’ll make informed decisions for your Deep Learning applications.

Deep Learning 52

Deep Learning Neural Network Machine Learning Natural Language Processing 52

Lexalytics

APRIL 5, 2021

They were not wrong: the results they found about the limitations of perceptrons still apply even to the more sophisticated deep-learning networks of today. 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.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Viso.ai

OCTOBER 7, 2024

billion tons of municipal solid waste was generated globally in 2016 with experts predicting a steep rise to 3.40 Computer vision mainly uses neural networks under the hood. Object Detection : Computer vision algorithms, such as convolutional neural networks (CNNs), analyze the images to identify and classify waste types (i.e.,

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Robotics 52

ODSC - Open Data Science

OCTOBER 4, 2024

His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, and deep reinforcement learning algorithms.

Data Science 52

Data Science Machine Learning Neural Network Software Engineer 52

AI Impacts

FEBRUARY 1, 2023

Preprint posted online June 21, 2016. The alignment problem from a deep learning perspective. link] 8 From Hacker News user api: “To give a specific example: I once wrote an objective function to train an evolving system to classify images, a simple machine learning test. New York, NY: W.W. link] 7 Ngo R.

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Viso.ai

DECEMBER 6, 2024

Object Detection with Deep Learning for traffic analytics with a video stream Vehicles can recognize the appearance of the cyclist, pedestrian, or car in front of them thanks to class-specific object detection. 2016) introduced a unified framework to detect both cyclists and pedestrians from images.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Deep Learning 52

Viso.ai

JANUARY 9, 2024

TensorFlow is one of the easiest computer vision tools and allows users to develop computer vision-related machine learning models for tasks like facial recognition , image classification, object detection , and more. Tensorflow, like OpenCV, also supports various languages like Python, C, C++, Java, or JavaScript. The Ultimate Overview.

Computer Vision 52

Computer Vision Deep Learning Neural Network Machine Learning 52

Viso.ai

JULY 17, 2024

Image analogies patch-based texture in-filling for artistic rendering – source The field of Neural style transfer took a completely new turn with Deep Learning. With deep learning, the results were impressively good. Here is the journey of NST. Gatys et al. 2015) The research paper by Leon A. Johnson et al.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Pickl AI

JULY 24, 2024

The advent of big data, coupled with advancements in Machine Learning and deep learning, has transformed the landscape of AI. Techniques such as neural networks, particularly deep learning, have enabled significant breakthroughs in image and speech recognition, natural language processing, and autonomous systems.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Natural Language Processing Neural Network 52

deepsense.ai

APRIL 16, 2023

This allows for the efficient processing of large amounts of data and can significantly reduce the time required for training deep learning models. 2016 ), only the activations at the boundaries of each partition are saved and shared between workers during training. Figure 1: Illustration of data parallelism.

Large Language Models 52

Large Language Models Neural Network Deep Learning Explainability 52

Explosion

JANUARY 12, 2018

We founded Explosion in October 2016, so this was our first full calendar year in operation. Declined 36 opportunities to “touch base” with investors and other professional networkers, who were confused by our radical we-spend-our-time-working approach. Here’s what we got done. cython-blis ?

NLP 52

NLP Neural Network Machine Learning Python 52

Mlearning.ai

MARCH 9, 2023

Recent studies have demonstrated that deep learning-based image segmentation algorithms are vulnerable to adversarial attacks, where carefully crafted perturbations to the input image can cause significant misclassifications (Xie et al., Towards deep learning models resistant to adversarial attacks. 2018; Sitawarin et al.,

Deep Learning 52

Deep Learning Computer Vision Machine Learning Algorithm 52

TheSequence

DECEMBER 13, 2023

My path to working in AI is somewhat unconventional and began when I was wrapping up a postdoc in theoretical particle physics around 2016. I was surprised to learn that a few lines of code could outperform features that had been carefully designed by physicists over many years. TheSequence is a reader-supported publication.

LLM 64

LLM Natural Language Processing Machine Learning Deep Learning 64

Viso.ai

MAY 20, 2023

It requires several times cheaper hardware than other neural networks and can be trained much faster on small datasets without any pre-trained weights. Most algorithms use a convolutional neural network (CNN) to extract features from the image to predict the probability of learned classes.

Algorithm 52

Algorithm Computer Vision Deep Learning Neural Network 52

AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). International Conference on Machine Learning. On large-batch training for deep learning: Generalization gap and sharp minima.” arXiv preprint arXiv:1609.04836 (2016). [3] PMLR, 2018. [2] 3] Dai, Wei, et al.

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Algorithm Deep Learning Neural Network Machine Learning 86

AWS Machine Learning Blog

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. Federated learning (FL) is a distributed ML approach that trains ML models on distributed datasets. The sample code supports horizontal and synchronous FL for training neural network models.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

As shown in the preceding figure, the ML paradigm is learning (training) followed by inference. In this example figure, features are extracted from raw historical data, which are then are fed into a neural network (NN). Due to model and data size, learning is distributed over multiple PBAs in an approach called parallelism.

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