Viso.ai

DECEMBER 3, 2023

This article will provide an introduction to object detection and provide an overview of the state-of-the-art computer vision object detection algorithms. The recent deep learning algorithms provide robust person detection results. Most modern person detector techniques are trained on frontal and asymmetric views.

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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. Is aqua the maximum?

Natural Language Processing 40

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Towards AI

JULY 19, 2023

Basically crack is a visible entity and so image-based crack detection algorithms can be adapted for inspection. Deep learning algorithms can be applied to solving many challenging problems in image classification. Deep learning algorithms can be applied to solving many challenging problems in image classification.

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Marktechpost

MAY 23, 2024

Applications of RL RL has been applied successfully in various domains: Gaming: RL algorithms have mastered complex games like Go, chess, and video games, often surpassing human experts. Robotics: RL enables robots to learn tasks such as grasping objects or navigating environments autonomously.

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Flipboard

JUNE 20, 2023

Rather than humans programming computers with specific step-by-step instructions on how to complete a task, in machine learning a human provides the AI with data and asks it to achieve a certain outcome via an algorithm. Deep learning Deep learning is a specific type of machine learning used in the most powerful AI systems.

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Unite.AI

JUNE 11, 2024

It was in 2014 when ICML organized the first AutoML workshop that AutoML gained the attention of ML developers. The primary reason behind limiting the range of machine learning models is to speed up the execution time of the LightAutoML framework without affecting the performance negatively for the given type of problem and data.

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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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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. NLP algorithms help computers understand, interpret, and generate natural language.

NLP 98

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Towards AI

AUGUST 11, 2023

GANs are a part of the deep-learning world and were very introduced by Ian Goodfellow and his collaborators in 2014, After that GANs have rapidly captivated many researchers’ eyes which resulted in much research and also helped to redefine the boundaries of creativity and artificial intelligence in the world of AI 1.1

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Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

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How to Learn Machine Learning

JULY 20, 2024

It falls under machine learning and uses deep learning algorithms and programs to create music, art, and other creative content based on the user’s input. However, significant strides were made in 2014 when Lan Goodfellow and his team introduced Generative adversarial networks (GANs).

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Becoming Human

MARCH 16, 2023

Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. AI began back in the 1950s as a simple series of “if, then rules” and made its way into healthcare two decades later after more complex algorithms were developed. AI drug discovery is exploding.

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Snorkel AI

JANUARY 10, 2023

In 2000, statistical algorithms came into existence, and these had the ability to handle tens of thousands of words. However, the problem with statistical algorithms was that, firstly, accuracy had reached a stable stage. So, you need to have that tight force alignment data to develop these statistical algorithm pipelines.

Deep Learning 52

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Heartbeat

FEBRUARY 27, 2023

Photo by Markus Spiske on Unsplash Deep learning has grown in importance as a focus of artificial intelligence research and development in recent years. Deep Reinforcement Learning (DRL) and Generative Adversarial Networks (GANs) are two promising deep learning trends.

Machine Learning 52

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Viso.ai

MAY 31, 2024

Our software helps several leading organizations start with computer vision and implement deep learning models efficiently with minimal overhead for various downstream tasks. As a result of Pascal VOC, researchers, and developers were able to compare various algorithms and methods on an entity basis. Get a demo here.

Computer Vision 52

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Flipboard

AUGUST 25, 2023

Zhavoronkov has a narrower definition of AI drug discovery, saying it refers specifically to the application of deep learning and generative learning in the drug discovery space. The “deep learning revolution” — a time when development and use of the technology exploded — took off around 2014, Zhavoronkov said.

Machine Learning 123

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Viso.ai

JUNE 26, 2024

This article will discuss the following: Neuromorphic Engineering and its core principles History and Development Algorithms Used How Neuromorphic Algorithms differ from Traditional Algorithms Real-world examples Applications and Use Cases About Us: At Viso.ai, we power Viso Suite, the most complete end-to-end computer vision platform.

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Viso.ai

JULY 17, 2024

Background and History of Neural Style Transfer NST is an example of an image styling problem that has been in development for decades, with image analogies and texture synthesis algorithms paving foundational work for NST. With deep learning, the results were impressively good. Here is the journey of NST. Gatys et al.

Convolutional Neural Networks 52

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Mlearning.ai

FEBRUARY 15, 2023

Uysal and Gunal, 2014). Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. Figure 11 Model Architecture The algorithms and models used for the first three classifiers are essentially the same.

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Mlearning.ai

JULY 31, 2023

Things become more complex when we apply this information to Deep Learning (DL) models, where each data type presents unique challenges for capturing its inherent characteristics. 2014; Bojanowski et al., Likewise, sound and text have no meaning to a computer. Instead, why not use a set of embeddings that are already trained?

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Viso.ai

DECEMBER 6, 2023

Modern computer vision research is producing novel algorithms for various applications, such as facial recognition, autonomous driving, annotated surgical videos, etc. Our solution enables leading companies to use a variety of machine learning models and tasks for their computer vision systems. Get a demo here.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Categorization 52

Heartbeat

OCTOBER 9, 2023

These ground-breaking areas redefine how we connect with and learn from our collective past. Computer vision algorithms can reconstruct a highly detailed 3D model by photographing objects from different perspectives. But computer vision algorithms can assist us in digitally scanning and preserving these priceless manuscripts.

Computer Vision 52

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Allen AI

JUNE 1, 2023

In 2014, in the early stages of the current AI boom, I was selected to participate in a winter school co-organized by my university and CMU that exposed me to deep learning frameworks. This provided me with the necessary spark to pursue a PhD in ML/AI at Georgia Tech. What is your favorite thing about working at AI2?

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AWS Machine Learning Blog

JANUARY 25, 2023

Apart from supporting explanations for tabular data, Clarify also supports explainability for both computer vision (CV) and natural language processing (NLP) using the same SHAP algorithm. Specifically, we show how you can explain the predictions of a text classification model that has been trained using the SageMaker BlazingText algorithm.

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AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). Another way can be to use an AllReduce algorithm. For example, in the ring-allreduce algorithm, each node communicates with only two of its neighboring nodes, thereby reducing the overall data transfers.

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Algorithm Deep Learning Neural Network Machine Learning 94

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 would change in 1986 with the publication of “Parallel Distributed Processing” [ 6 ], which included a description of the backpropagation algorithm [ 7 ].

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Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Dlabs.ai

AUGUST 20, 2020

If you’re looking for the best free eBooks related to artificial intelligence, machine learning, or deep learning – this list is for you. Dive into Deep Learning Authors: Aston Zhang, Zack C. Smola The first eBook on our must-read list is a deep-dive into deep learning.

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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

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ML Review

MARCH 31, 2019

In 2014, a group of researchers at Google and NYU found that it was far too easy to fool ConvNets with an imperceivable, but carefully constructed nudge in the input. Up to this point, machine learning algorithms simply didn’t work well enough for anyone to be surprised when it failed to do the right thing. Sharif et al.

Neural Network 52

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. Also in patient monitoring, image guided therapy, ultrasound and personal health teams have been creating ML algorithms and applications.

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John Snow Labs

AUGUST 7, 2024

Generative AI in healthcare is a transformative technology that utilizes advanced algorithms to synthesize and analyze medical data, facilitating personalized and efficient patient care. A significant milestone was reached in 2014 with the introduction of Generative Adversarial Networks (GANs).

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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. Disease Risk Modelling Healthcare organizations use machine learning to model the risk of diabetes in a subset of a population.

Artificial Intelligence 52

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Viso.ai

JANUARY 2, 2024

VGGNet , introduced by Simonyan and Zisserman in 2014, emphasized the importance of depth in CNN architectures through its 16-19 layer CNN network. Although primarily known as an object detection algorithm, YOLO uses a CNN as its backbone for feature extraction. This contributes to its efficiency in real-time object detection tasks.

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PyImageSearch

OCTOBER 2, 2023

Jump Right To The Downloads Section A Deep Dive into Variational Autoencoder with PyTorch Introduction Deep learning has achieved remarkable success in supervised tasks, especially in image recognition. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

Computer Vision 114

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John Snow Labs

FEBRUARY 29, 2024

Generative AI in healthcare is a transformative technology that utilizes advanced algorithms to synthesize and analyze medical data, facilitating personalized and efficient patient care. A significant milestone was reached in 2014 with the introduction of Generative Adversarial Networks (GANs).

Generative AI 40

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AWS Machine Learning Blog

JANUARY 13, 2023

They were admitted to one of 335 units at 208 hospitals located throughout the US between 2014–2015. The eICU data is ideal for developing ML algorithms, decision support tools, and advancing clinical research. His research focuses on distributed/federated machine learning algorithms, systems, and applications.

Machine Learning 102

Machine Learning ML Categorization Deep Learning 102

ODSC - Open Data Science

JANUARY 29, 2024

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. Open Source ML/DL Platforms: Pytorch, Tensorflow, and scikit-learn Hiring managers continue to favor the most popular open-source machine/deep learning platforms including Pytorch, Tensorflow, and scikit-learn.

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