Marktechpost

SEPTEMBER 10, 2023

Researchers think that high-speed testing using Deep Learning models can help us understand these effects better and speed up catalyst development. The way a catalyst’s surface is shaped matters for certain chemical reactions due to various properties of the catalyst, which we study in Surface Chemistry.

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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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AI Weekly

JANUARY 2, 2025

In the last year, AI has also been associated with the latest technology revolution for generative AI, large language models, and solutions that promise to change the way we do work, process information and interface with electronic technology in general. moderndiplomacy.eu decrypt.co

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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. These include object identification, object recognition, image segmentation, and edge detection.

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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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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

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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Marktechpost

JANUARY 13, 2024

The team’s primary goal was to shed light on the model’s intrinsic qualities that do not require further training or fine-tuning so that practitioners can make informed decisions when working with pre-trained models. They find that various topologies and training paradigms exhibit significantly varied model behaviors.

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Unite.AI

JULY 23, 2024

By leveraging advances in artificial intelligence (AI) and neuroscience, researchers are developing systems that can translate the complex signals produced by our brains into understandable information, such as text or images. These patterns are then decoded using deep neural networks to reconstruct the perceived images.

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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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Pickl AI

SEPTEMBER 5, 2024

Summary: Convolutional Neural Networks (CNNs) are essential deep learning algorithms for analysing visual data. They automatically extract and learn features, making them ideal for tasks like image classification and object detection. What are Convolutional Neural Networks?

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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.

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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. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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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

MARCH 16, 2025

Despite advancements in deep learning , many existing stereo-matching models require domain-specific fine-tuning to achieve high accuracy. These methods utilize 3D convolutional neural networks (CNNs) for cost filtering but struggle with generalization beyond their training data.

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AWS Machine Learning Blog

MARCH 21, 2025

Research papers and engineering documents often contain a wealth of information in the form of mathematical formulas, charts, and graphs. Navigating these unstructured documents to find relevant information can be a tedious and time-consuming task, especially when dealing with large volumes of data. samples/2003.10304/page_0.png'

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Towards AI

SEPTEMBER 1, 2023

That’s where Machine Learning, and specifically Deep Learning, shines, abbreviating the processing time to mere minutes! The downside is that, when reducing the dimensionality this way, there’s an intrinsic loss of information, and the models become less generalizable when facing data outside of the design space.

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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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Towards AI

JULY 31, 2023

Photo by Tobias Reich on Unsplash In the ever-evolving world of artificial intelligence, Convolutional Neural Networks (CNNs) have emerged as a revolutionary technology, reshaping the fields of computer vision and image recognition. Filters, also known as kernels. red, green, blue). red, green, blue).

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Unite.AI

OCTOBER 17, 2024

Some of the earliest and most extensive work has occurred in the use of deep learning and computer vision models. During training, each row of data as it passes through the network–called a neural network–modifies the equations at each layer of the network so that the predicted output matches the actual output.

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Marktechpost

SEPTEMBER 1, 2023

Smartphones now hold immense amounts of sensitive information, making security a pressing concern. Incorporating machine learning and deep learning algorithms has shown promise in bolstering security. Researchers have been exploring behavioral and physiological biometrics for enhancing mobile device security.

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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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Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

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ODSC - Open Data Science

APRIL 28, 2023

Each has a single representation for the word “well”, which combines the information for “doing well” with “wishing well”. In this post, I’ll be demonstrating two deep learning approaches to sentiment analysis. Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e.

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Viso.ai

JANUARY 2, 2024

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Whether you’re a beginner or an experienced practitioner, this guide will provide insights into the mechanics of artificial neural networks and their applications. Howard et al.

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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).

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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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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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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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Pickl AI

NOVEMBER 22, 2024

Summary: Long Short-Term Memory (LSTM) networks are a specialised form of Recurrent Neural Networks (RNNs) that excel in learning long-term dependencies in sequential data. By utilising memory cells and gating mechanisms, LSTMs effectively manage information flow, preventing issues like the vanishing gradient problem.

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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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ODSC - Open Data Science

SEPTEMBER 18, 2024

Convolutional neural networks (CNNs) differ from conventional, fully connected neural networks (FCNNs) because they process information in distinct ways. CNNs use a three-dimensional convolution layer and a selective type of neuron to compute critical artificial intelligence processes.

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Viso.ai

NOVEMBER 5, 2024

This bottom-up path aggregates and passes features from lower levels back up through the network, which reinforces lower-level features with contextual information and enriches high-level features with spatial details. This step outputs both global and local information from the feature maps.

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Viso.ai

DECEMBER 3, 2023

The goal of object detection is to develop computational models that provide the most fundamental information needed by computer vision applications : “ What objects are where ?” The recent deep learning algorithms provide robust person detection results.

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John Snow Labs

JUNE 2, 2023

Sentiment analysis, also known as opinion mining, is the process of computationally identifying and categorizing the subjective information contained in natural language text. Deep learning models can automatically learn features and representations from raw text data, making them well-suited for sentiment analysis tasks.

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Towards AI

JULY 9, 2024

With the utilization of this technology, geographic information systems (GIS) and remote sensing can become much more comprehensive, which will enable the process of tracking changes over time, assessing the effects of human activity, and reaching well-informed judgments for sustainable development.

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