AI News

JULY 27, 2023

Gcore trained a Convolutional Neural Network (CNN) – a model designed for image analysis – using the CIFAR-10 dataset containing 60,000 labelled images, on these devices. The results were striking, with IPUs and GPUs significantly outperforming CPUs in training speed. The event is co-located with Digital Transformation Week.

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Marktechpost

FEBRUARY 12, 2024

Deep convolutional neural networks (DCNNs) have been a game-changer for several computer vision tasks. Network depth and convolution are the two primary components of a DCNN that determine its expressive power. Join our 36k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup.

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Marktechpost

JULY 15, 2024

Robustness is crucial for deploying deep learning models in real-world applications. Recent advancements in large kernel convolutions have revived interest in CNNs, showing they can match or exceed ViT performance. Join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter.

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Marktechpost

JANUARY 13, 2024

Join our 36k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup. They recommend the use of CLIP models in the event of a significant domain transition. Check out the Paper, Project, and Github. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter.

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AI Weekly

SEPTEMBER 12, 2024

usnews.com Sponsor Join dotAI the world's brightest AI conference for tech engineers Whether you’re a developer, engineer, data scientist, ML specialist, CTO, or tech enthusiast, dotAI 2024 is your opportunity to hear from the best engineers out there, not from those who are just talking about change, but those who are building it!

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Unite.AI

NOVEMBER 6, 2023

With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? In this article, we’ll look at the state of the traditional machine learning landscape concerning modern generative AI innovations. What is Traditional Machine Learning?

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AI Weekly

MARCH 21, 2024

nature.com The lucent yet opaque challenge of regulating AI in radiology Here we discuss the current and future regulatory landscapes of AI/ML in radiology, and we highlight pressing challenges that are critical for regulatory bodies to traverse.

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Marktechpost

MAY 7, 2024

Current computerized systems employ machine learning techniques, from shallow learning that relies on hand-crafted features to more advanced deep learning models that extract features directly from raw EEG data. These technologies aim to mimic the precision of human analysts while surpassing their speed and endurance.

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Marktechpost

APRIL 24, 2024

While traditional PAM analysis is time-consuming, recent advancements in deep learning technology offer promising solutions for automating bird species identification from audio recordings. Preliminary research in interpretable deep learning for audio includes deep prototype learning, initially proposed for image classification.

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Marktechpost

DECEMBER 29, 2023

Consequently, the researchers of Plant Phenomics have introduced BarbNet, a deep-learning model designed specifically for the automated detection and phenotyping of barbs in microscopic images of awns. Despite their importance is evident, analyzing these small structures has been challenging due to the lack of automated tools.

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Marktechpost

NOVEMBER 16, 2023

A new research paper presents a deep learning-based classifier for age-related macular degeneration (AMD) stages using retinal optical coherence tomography (OCT) scans. The research details creating a deep learning-based system for automated AMD detection and staging using retinal OCT scans. in a real-world test set.

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Marktechpost

JANUARY 15, 2025

This gap has led to the evolution of deep learning models, designed to learn directly from raw data. What is Deep Learning? Deep learning, a subset of machine learning, is inspired by the structure and functioning of the human brain. High Accuracy: Delivers superior performance in many tasks.

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The MLOps Blog

NOVEMBER 14, 2023

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. This is where visualizations in ML come in.

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Marktechpost

JANUARY 14, 2024

There has been a meteoric rise in the use of deep learning in image processing in the past several years. The robust feature learning and mapping capabilities of deep learning-based approaches enable them to acquire intricate blur removal patterns from large datasets. Also, don’t forget to follow us on Twitter.

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Heartbeat

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs learn geometric properties on different scales by applying convolutional filters to input data.

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Marktechpost

NOVEMBER 30, 2023

Addressing this challenge, a study by researchers from Tokyo University of Science (TUS), Japan, in collaboration with esteemed institutions, introduced a new deep learning model. The researchers constructed a binary classifier employing 80 convolutional neural networks.

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Marktechpost

JANUARY 16, 2024

Person Re-identification (Person Re-ID) in Machine Learning uses deep learning models like convolutional neural networks to recognize and track individuals across different camera views, holding promise for surveillance and public safety but raising significant privacy concerns.

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Marktechpost

SEPTEMBER 1, 2023

Incorporating machine learning and deep learning algorithms has shown promise in bolstering security. In addition, the study highlights that hybrid schemes combining deep learning features with deep learning/machine learning classification can significantly improve authentication performance.

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Marktechpost

OCTOBER 10, 2023

Deep learning, a machine learning subset, automatically learns complex representations from the input. Convolutional neural networks (CNNs) and vision transformers (ViT), two examples of deep learning models for computer vision, analyze signals by assuming planar (flat) regions.

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Marktechpost

SEPTEMBER 24, 2024

Previously, researchers doubted that neural networks could solve complex visual tasks without hand-designed systems. However, this work demonstrated that with sufficient data and computational resources, deep learning models can learn complex features through a general-purpose algorithm like backpropagation.

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Marktechpost

DECEMBER 15, 2023

In this manner, from coarsely resolved data, the GAN learns how to produce realistic precipitation fields and determine their temporal sequence. Compared to trilinear interpolation and a classical convolutional neural network, the generative model reconstructs the resolution-dependent extreme value distribution with high skill.

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Heartbeat

DECEMBER 20, 2023

We delve into the intricacies of Residual Networks (ResNet), a groundbreaking architecture in CNNs. Understanding why ResNet is essential, its innovative aspects, and what it enables in deep learning forms a crucial part of our exploration. Why We Need ResNet Let’s imagine that we had a shallow network that was performing well.

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Towards AI

JULY 9, 2024

Deep Learning-Based Algorithms: -Convolutional Neural Network: Because convolutional neural networks (CNNs) can directly learn hierarchical features from data, they are frequently employed for object detection in the case of spatial analysis and machine learning.

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Marktechpost

SEPTEMBER 4, 2023

Recent advancements in deep neural networks have enabled new approaches to address anatomical segmentation. For instance, state-of-the-art performance in the anatomical segmentation of biomedical images has been attained by deep convolutional neural networks (CNNs). Check out the Paper and Github.

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Marktechpost

MARCH 22, 2024

The advent of deep learning propelled the field forward, offering improved accuracy but at the expense of computational efficiency. This hierarchical refinement process is pivotal in achieving high precision without the burdensome computational cost of deep learning methods. Also, don’t forget to follow us on Twitter.

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Towards AI

JULY 19, 2023

How to Start Your ML Journey? | | Results from LinkedIn Polls Photo by Susan Q Yin / Unsplash Someone trying to start their ML journey would be confused about where to start. A few ML Memes that I found funny. Hence, I conducted polls in different ML groups for the two questions. What is important?

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Heartbeat

MARCH 1, 2023

Tracking your image classification experiments with Comet ML Photo from nmedia on Shutterstock.com Introduction Image classification is a task that involves training a neural network to recognize and classify items in images. Before being fed into the network, the photos are pre-processed and shrunk to the same size.

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Marktechpost

MAY 26, 2024

Efficient deep learning accelerators, particularly for edge processing, benefit from IMC by reducing data movement costs and enhancing energy efficiency and latency, necessitating automated optimization of numerous design parameters. The Internet of Things (IoT) increases data generation, demanding advanced AI processing capabilities.

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Marktechpost

MAY 5, 2024

In recent years, the demand for AI and Machine Learning has surged, making ML expertise increasingly vital for job seekers. Additionally, Python has emerged as the primary language for various ML tasks. Participants also gain hands-on experience with open-source frameworks and libraries like TensorFlow and Scikit-learn.

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Marktechpost

NOVEMBER 20, 2024

Some researchers have introduced multi-architectural modular deep neural networks to reduce false positives in anomaly detection. Others have proposed a hybrid network intrusion detection system integrating convolutional neural networks (CNN), fuzzy C-means clustering, genetic algorithm, and a bagging classifier.

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Marktechpost

DECEMBER 12, 2023

The FGS has been used to predict facial landmark placements and pain scores by utilizing deep neural networks and machine learning models. The post These Fully Automated Deep Learning Models Can Be Used For Pain Prediction Using The Feline Grimace Scale (FGS) With Smartphone Integration appeared first on MarkTechPost.

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Viso.ai

DECEMBER 3, 2023

How pose estimation works: Deep learning methods Use Cases and pose estimation applications How to get started with AI motion analysis Real-time full body pose estimation in construction – built with Viso Suite About us: Viso.ai Today, the most powerful image processing models are based on convolutional neural networks (CNNs).

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Heartbeat

NOVEMBER 14, 2023

Integrating XGboost with Convolutional Neural Networks Photo by Alexander Grey on Unsplash XGBoost is a powerful library that performs gradient boosting. It has an excellent reputation as a tool for predicting many kinds of problems in data science and machine learning. It was envisioned by Thongsuwan et al.,

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Viso.ai

DECEMBER 19, 2023

Today, the use of convolutional neural networks (CNN) is the state-of-the-art method for image classification. Image Classification Using Machine Learning CNN Image Classification (Deep Learning) Example applications of Image Classification Let’s dive deep into it! How Does Image Classification Work?

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Marktechpost

SEPTEMBER 12, 2023

It leverages the capabilities of deep learning models, such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs). Traditional I2I methods have primarily focused on translating between domains with small gaps, such as photos to paintings or different types of animals.

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Heartbeat

MAY 2, 2023

Artificial intelligence has undergone a revolution thanks to deep learning. Deep learning allows machines to learn from vast amounts of data and carry out complex tasks that were previously only considered possible by humans (like translation between languages, recognizing objects etc.).

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Marktechpost

DECEMBER 24, 2023

By addressing the limitations observed in prior surveys, this comprehensive study aims to bridge the gap by encompassing a broader spectrum of ML techniques, from traditional to deep learning and augmented learning. Deep learning methods have shown robust performance in classifying leaf diseases.

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