Unite.AI

MAY 8, 2024

The ability to effectively represent and reason about these intricate relational structures is crucial for enabling advancements in fields like network science, cheminformatics, and recommender systems. Graph Neural Networks (GNNs) have emerged as a powerful deep learning framework for graph machine learning tasks.

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Analytics Vidhya

MAY 25, 2023

Enter autoencoders, deep learning‘s hidden heroes. These interesting neural networks can compress, reconstruct, and extract important information from data. Introduction Extracting important insights from complicated datasets is the key to success in the era of data-driven decision-making.

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Marktechpost

JANUARY 24, 2024

Artificial Intelligence has witnessed a revolution, largely due to advancements in deep learning. This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. Before the advent of deep learning, data representation often involved manually curated feature vectors.

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Pickl AI

NOVEMBER 24, 2024

Summary: Autoencoders are powerful neural networks used for deep learning. Their applications include dimensionality reduction, feature learning, noise reduction, and generative modelling. An autoencoder is a neural network designed to learn a compressed representation of input data.

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Analytics Vidhya

JULY 25, 2022

Introduction Computer Vision Is one of the leading fields of Artificial Intelligence that enables computers and systems to extract useful information from digital photos, movies, and other visual inputs. It uses Machine Learning-based Model Algorithms and Deep Learning-based Neural Networks for its implementation. […].

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Unite.AI

OCTOBER 10, 2024

A new research paper from Canada has proposed a framework that deliberately introduces JPEG compression into the training scheme of a neural network, and manages to obtain better results – and better resistance to adversarial attacks. In contrast, JPEG-DL (right) succeeds in distinguishing and delineating the subject of the photo.

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Unite.AI

MARCH 2, 2025

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. For years, deep learning has relied on traditional dense layers, where every neuron in one layer is connected to every neuron in the next.

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Unite.AI

JANUARY 18, 2025

While Central Processing Units (CPUs) and Graphics Processing Units (GPUs) have historically powered traditional computing tasks and graphics rendering, they were not originally designed to tackle the computational intensity of deep neural networks.

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Pickl AI

JANUARY 26, 2025

Summary: Deep Learning models revolutionise data processing, solving complex image recognition, NLP, and analytics tasks. Introduction Deep Learning models transform how we approach complex problems, offering powerful tools to analyse and interpret vast amounts of data. With a projected market growth from USD 6.4

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Analytics Vidhya

DECEMBER 6, 2021

This article was published as a part of the Data Science Blogathon Introduction Long short term memory (LSTM) is a model that increases the memory of recurrent neural networks. Recurrent neural networks hold short term memory in that they allow earlier determining information to be employed in the current neural networks.

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Marktechpost

OCTOBER 28, 2024

By inputting different prompts, users can observe the model’s ability to generate human-quality text, translate languages, write various kinds of creative content, and answer your questions in an informative way. It’s a valuable tool for anyone interested in learning about deep learning and machine learning.

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AI Weekly

OCTOBER 26, 2023

cryptopolitan.com Applied use cases Alluxio rolls out new filesystem built for deep learning Alluxio Enterprise AI is aimed at data-intensive deep learning applications such as generative AI, computer vision, natural language processing, large language models and high-performance data analytics. voxeurop.eu

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Becoming Human

MARCH 16, 2023

Photo by Paulius Andriekus on Unsplash Welcome back to the next part of this Blog Series on Graph Neural Networks! The following section will provide a little introduction to PyTorch Geometric , and then we’ll use this library to construct our very own Graph Neural Network!

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Marktechpost

FEBRUARY 8, 2024

With the world of computational science continually evolving, physics-informed neural networks (PINNs) stand out as a groundbreaking approach for tackling forward and inverse problems governed by partial differential equations (PDEs). Despite these efforts, the search for an optimal solution remains ongoing.

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NVIDIA

MARCH 19, 2025

Everybody at NVIDIA is incentivized to figure out how to work together because the accelerated computing work that NVIDIA does requires full-stack optimization, said Bryan Catanzaro, vice president of applied deep learning research at NVIDIA. Learn more about NVIDIA Research at GTC.

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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. On the other hand, SSMs are a promising approach for modeling sequential data in deep learning.

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Marktechpost

MAY 2, 2024

Multi-layer perceptrons (MLPs), or fully-connected feedforward neural networks, are fundamental in deep learning, serving as default models for approximating nonlinear functions. Thus, while MLPs remain crucial, there’s ongoing exploration for more effective nonlinear regressors in neural network design.

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Unite.AI

AUGUST 28, 2024

in Information Systems Engineering from Ben Gurion University and an MBA from the Technion, Israel Institute of Technology. Deep Instinct is a cybersecurity company that applies deep learning to cybersecurity. Deep Instinct uses a unique deep learning framework for its cybersecurity solutions.

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Marktechpost

DECEMBER 10, 2024

Artificial Neural Networks (ANNs) have become one of the most transformative technologies in the field of artificial intelligence (AI). Modeled after the human brain, ANNs enable machines to learn from data, recognize patterns, and make decisions with remarkable accuracy. How Do Artificial Neural Networks Work?

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Marktechpost

APRIL 12, 2024

Deep learning architectures have revolutionized the field of artificial intelligence, offering innovative solutions for complex problems across various domains, including computer vision, natural language processing, speech recognition, and generative models. This state is updated as the network processes each element of the sequence.

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AI Weekly

AUGUST 10, 2023

siliconangle.com Sponsor Make Smarter Business Decisions with The Information Looking for a competitive edge in the world of business? The Information offers access to exclusive insights from the industry's top journalists. For a limited time, subscribe and save 25% on your first year. Subscribe today!] 1.41%) (BRK.B 1.41%) (BRK.B

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Marktechpost

SEPTEMBER 3, 2024

Neuroscience reveals that neurons interact through various connectivity patterns, known as circuit motifs, which are crucial for processing information. Recent neural architectures remain inspired by biological nervous systems but lack the complex connectivity found in the brain, such as local density and global sparsity.

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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. This approach leverages GNNs to learn the underlying graph structure of an environment, enabling it to make quick and informed decisions about which paths to take.

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TensorFlow

FEBRUARY 6, 2024

Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases.

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Unite.AI

JUNE 25, 2023

However, assimilating the understanding of physics into the realm of neural networks has proved challenging. In a significant breakthrough, the UCLA study intends to combine the deep understanding from data and the real-world know-how of physics, thereby creating a hybrid AI with augmented capabilities. .”

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Marktechpost

JANUARY 9, 2024

Deep learning methods have been widely employed for early disease detection to tackle this challenge, showcasing remarkable classification accuracy and data synthesis to bolster model training. The study acknowledges the limited research effort in investigating multimodal images related to breast cancer using deep learning techniques.

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Marktechpost

DECEMBER 25, 2023

One of the major issues with CDI, though, is the phase retrieval problem, where the detectors fail to record the phase of the diffracted wave, leading to information loss. A considerable amount of research has been done to address this problem, focusing mainly on using artificial neural networks.

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AI Weekly

NOVEMBER 23, 2023

forbes.com Applied use cases From Data To Diagnosis: A Deep Learning Approach To Glaucoma Detection When the algorithm is implemented in clinical practice, clinicians collect data such as optic disc photographs, visual fields, and intraocular pressure readings from patients and preprocess the data before applying the algorithm to diagnose glaucoma.

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Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

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Marktechpost

NOVEMBER 25, 2023

Addressing this, Jason Eshraghian from UC Santa Cruz developed snnTorch, an open-source Python library implementing spiking neural networks, drawing inspiration from the brain’s remarkable efficiency in processing data. Traditional neural networks lack the elegance of the brain’s processing mechanisms.

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Marktechpost

MAY 8, 2024

Over the past decade, advancements in deep learning and artificial intelligence have driven significant strides in self-driving vehicle technology. Deep learning and AI technologies play crucial roles in both modular and End2End systems for autonomous driving. Classical methodologies for these tasks are also explored.

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Analytics Vidhya

FEBRUARY 16, 2022

that deals with deriving meaningful information from images. Since 2012 after convolutional neural networks(CNN) were introduced, we moved away from handcrafted features to an end-to-end approach using deep neural networks. Introduction Computer vision is a field of A.I. These are easy to develop […].

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AssemblyAI

SEPTEMBER 4, 2023

Data compression plays a pivotal role in today’s digital world, facilitating efficient storage and transmission of information. Neural compression methods can achieve near-lossless capabilities when combined with upscaling and super-resolution techniques for reconstruction ( source ). The EnCodec architecture ( source ).

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Unite.AI

DECEMBER 26, 2024

These deep learning algorithms get data from the gyroscope and accelerometer inside a wearable device ideally worn around the neck or at the hip to monitor speed and angular changes across three dimensions.

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Unite.AI

DECEMBER 8, 2023

These systems, typically deep learning models, are pre-trained on extensive labeled data, incorporating neural networks for self-attention. Each neuron in every layer of a fast feedforward network is interconnected with every neuron in the next layer, thus making FFF neural networks a fully connected network.

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Marktechpost

OCTOBER 8, 2023

The brain is hierarchically organized, with lower-level sensory processing areas sending information to higher-level cognitive and decision-making regions. The brain processes information in parallel, with different regions and networks simultaneously working on various aspects of perception, cognition, and motor control.

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Towards AI

NOVEMBER 19, 2023

Photo by Pietro Jeng on Unsplash Deep learning is a type of machine learning that utilizes layered neural networks to help computers learn from large amounts of data in an automated way, much like humans do. We will explain intuitively what each one means and how it contributes to the deep learning process.

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