Unite.AI

JULY 31, 2023

In the past decade, Artificial Intelligence (AI) and Machine Learning (ML) have seen tremendous progress. Modern AI and ML models can seamlessly and accurately recognize objects in images or video files. The SEER model by Facebook AI aims at maximizing the capabilities of self-supervised learning in the field of computer vision.

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Towards AI

MARCH 7, 2025

Originally published on Towards AI. Limitations of ANNs: Move to Convolutional Neural Networks This member-only story is on us. The journey from traditional neural networks to convolutional architectures wasnt just a technical evolution it was a fundamental reimagining of how machines should perceive visual information.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

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Unite.AI

NOVEMBER 5, 2024

The Artificial Intelligence (AI) chip market has been growing rapidly, driven by increased demand for processors that can handle complex AI tasks. The need for specialized AI accelerators has increased as AI applications like machine learning, deep learning , and neural networks evolve. trade restrictions.

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Marktechpost

MARCH 23, 2024

Deep features are pivotal in computer vision studies, unlocking image semantics and empowering researchers to tackle various tasks, even in scenarios with minimal data. With their transformative potential, deep features continue to push the boundaries of what’s possible in computer vision.

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Unite.AI

OCTOBER 17, 2024

Much work remains to be done to understand the safest and most effective applications of AI in healthcare, to build trust among clinicians in the use of AI, and to adjust our clinical education system to drive better use of AI-based systems. Several types of networks exist. First, some terminology.

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Unite.AI

AUGUST 19, 2024

Neural networks have been at the forefront of AI advancements, enabling everything from natural language processing and computer vision to strategic gameplay, healthcare, coding, art and even self-driving cars. The structure typically includes an input layer, one or more hidden layers, and an output layer.

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Unite.AI

MARCH 2, 2025

Artificial Intelligence (AI) is evolving at an unprecedented pace, with large-scale models reaching new levels of intelligence and capability. From early neural networks to todays advanced architectures like GPT-4 , LLaMA , and other Large Language Models (LLMs) , AI is transforming our interaction with technology.

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Unite.AI

DECEMBER 26, 2024

AI is being applied to a wide range of the worlds problems among them, keeping the elderly safe as they age. AI is behind numerous technologies that enable seamless, accurate, and personalized monitoring, allowing seniors to age at home confidently and safely. Fortunately, Artificial Intelligence can help meet this challenge.

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NVIDIA

DECEMBER 19, 2024

An AI app by Ulta Beauty, the largest specialty beauty retailer in the U.S., Used by thousands of web and mobile app users daily, the experience is powered by the NVIDIA StyleGAN2 generative AI model. Shoppers pondering a new hairstyle can now try styles before committing to curls or a new color.

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Towards AI

DECEMBER 16, 2024

Author(s): Prashant Kalepu Originally published on Towards AI. The Top 10 AI Research Papers of 2024: Key Takeaways and How You Can Apply Them Photo by Maxim Tolchinskiy on Unsplash As the curtains draw on 2024, its time to reflect on the innovations that have defined the year in AI. And lets be real what a year it has been!

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Analytics Vidhya

JULY 4, 2023

Introduction Denoising Autoencoders are neural network models that remove noise from corrupted or noisy data by learning to reconstruct the initial data from its noisy counterpart. We can stack these autoencoders together to form deep networks, increasing their performance.

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Towards AI

NOVEMBER 10, 2024

Last Updated on November 10, 2024 by Editorial Team Author(s): Tata Ganesh Originally published on Towards AI. 4] claim that all samples in a dataset are not equally important for neural network training. They propose an importance sampling technique to focus computation on “informative” examples during training.

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AI Weekly

JULY 13, 2023

In the News Elon Musk unveils new AI company set to rival ChatGPT Elon Musk, who has hinted for months that he wants to build an alternative to the popular ChatGPT artificial intelligence chatbot, announced the formation of what he’s calling xAI, whose goal is to “understand the true nature of the universe.” Powered by pluto.fi theage.com.au

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Analytics Vidhya

APRIL 5, 2022

Introduction Style transfer is a developing field in neural networks and it is a very useful feature that can be integrated into social media and AI apps. Several neural networks can map and transfer image styles to an input image as per the training […].

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Marktechpost

MAY 14, 2024

This article lists the top Deep Learning and Neural Networks books to help individuals gain proficiency in this vital field and contribute to its ongoing advancements and applications. Neural Networks and Deep Learning The book explores both classical and modern deep learning models, focusing on their theory and algorithms.

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Marktechpost

JANUARY 23, 2024

There are two major challenges in visual representation learning: the computational inefficiency of Vision Transformers (ViTs) and the limited capacity of Convolutional Neural Networks (CNNs) to capture global contextual information. A team of researchers at UCAS, in collaboration with Huawei Inc.

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Unite.AI

JANUARY 22, 2025

AI practice management solutions are improving healthcare operations through automation and intelligent processing. Today's healthcare organizations can choose from various AI solutions tailored to specific operational needs. Today's healthcare organizations can choose from various AI solutions tailored to specific operational needs.

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Marktechpost

AUGUST 3, 2024

However, these neural networks face challenges in interpretation and scalability. The difficulty in understanding learned representations limits their transparency, while expanding the network scale often proves complex. The study also investigates the impact of activation functions on network performance, particularly B-spline.

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NVIDIA

MARCH 3, 2025

From Seattle, Washington, to Cape Town, South Africa and everywhere around and between AI is helping conserve the wild plants and animals that make up the intricate web of life on Earth. Our own survival depends on wildlife, the above video on this years celebration says, just as much as their survival depends on us.

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Marktechpost

MAY 6, 2024

Convolutional Neural Networks (CNNs) have become the benchmark for computer vision tasks. Capsule Networks (CapsNets), first introduced by Hinton et al. Despite computational complexity and optimization challenges, ongoing research continues to enhance CapsNets’ performance and efficiency.

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Marktechpost

JANUARY 24, 2024

This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. The reach of these transformations extends beyond the confines of computer science, influencing diverse fields such as robotics, biology, and chemistry, showcasing the pervasive impact of AI across various disciplines.

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Marktechpost

SEPTEMBER 2, 2023

In the ever-evolving field of computer vision, a pressing concern is the imperative to ensure fairness. Researchers from Meta AI emphasize the crucial equilibrium that must be struck—a harmonious balance between the rapid cadence of innovation and the conscientious development practices that emerge as necessary.

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Analytics Vidhya

JUNE 4, 2023

In a groundbreaking development, NVIDIA Research has unveiled its latest AI model, Neuralangelo. The innovative model utilizes neural networks to reconstruct 3D scenes and objects from 2D video clips.

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Marktechpost

JANUARY 3, 2024

In the swiftly evolving domain of computer vision, the breakthrough in transforming a single image into a 3D object structure is a beacon of innovation. To address these limitations, a method known as Hyper-VolTran has been introduced by researchers from Meta AI. Check out the Paper.

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Marktechpost

DECEMBER 23, 2023

Deep Neural Networks (DNNs) represent a powerful subset of artificial neural networks (ANNs) designed to model complex patterns and correlations within data. These sophisticated networks consist of multiple layers of interconnected nodes, enabling them to learn intricate hierarchical representations.

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Marktechpost

APRIL 4, 2024

A deep Neural network is crucial in synthesizing photorealistic images and videos using large-scale image and video generative models. Also, the neural network weight, convolution, or linear layers remain the same for different conditions. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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AI Weekly

SEPTEMBER 21, 2023

pitneybowes.com In The News How Google taught AI to doubt itself Today let’s talk about an advance in Bard, Google’s answer to ChatGPT, and how it addresses one of the most pressing problems with today’s chatbots: their tendency to make things up. [Get your FREE eBook.] Get your FREE eBook.] Get your FREE eBook.] Get your FREE eBook.]

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Marktechpost

SEPTEMBER 17, 2024

In deep learning, neural network optimization has long been a crucial area of focus. Training large models like transformers and convolutional networks requires significant computational resources and time. Optimizing the training process is critical for deploying AI applications more quickly and efficiently.

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Marktechpost

MAY 5, 2024

Graph Neural Network (GNN)–based motion planning has emerged as a promising approach in robotic systems for its efficiency in pathfinding and navigation tasks. Path Quality: Paths are optimized based on the latent space encoded by the network. Let’s delve into the detailed specifics of the three prominent systems: 1.

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AI News

AUGUST 9, 2023

Microsoft Azure users are now able to harness the latest advancements in NVIDIA’s accelerated computing technology, revolutionising the training and deployment of their generative AI applications. This capability positions the technology on par with the computational capabilities of the world’s most advanced supercomputers.

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Marktechpost

DECEMBER 30, 2023

The remarkable potentials of Artificial Intelligence (AI) and Deep Learning have paved the way for a variety of fields ranging from computer vision and language modeling to healthcare, biology, and whatnot. Operator learning includes creating an optimization problem in order to find the ideal neural network parameters.

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Marktechpost

MAY 13, 2024

Vision Transformers (ViT) and Convolutional Neural Networks (CNN) have emerged as key players in image processing in the competitive landscape of machine learning technologies. The Rise of Vision Transformers (ViTs) Vision Transformers represent a revolutionary shift in how machines process images.

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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. Looking at the big picture, establishing numerous (Bi-)PDC instances optimally can improve a network.

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Towards AI

NOVEMBER 2, 2024

Author(s): Gennaro Daniele Acciaro Originally published on Towards AI. Indeed, after obtaining a neural network that accurately predicts all the test data, it remains useless unless it’s made accessible to the world. In this guide, we’ll walk through the process of deploying a custom model trained using the Detectron2 framework.

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Marktechpost

JUNE 30, 2024

Deep learning models like Convolutional Neural Networks (CNNs) and Vision Transformers achieved great success in many visual tasks, such as image classification, object detection, and semantic segmentation.

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Unite.AI

NOVEMBER 6, 2023

In recent years, Generative AI has shown promising results in solving complex AI tasks. Modern AI models like ChatGPT , Bard , LLaMA , DALL-E.3 Moreover, Multimodal AI techniques have emerged, capable of processing multiple data modalities, i.e., text, images, audio, and videos simultaneously. What are its Limitations?

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