Analytics Vidhya

NOVEMBER 6, 2020

The post A Short Intuitive Explanation of Convolutional Recurrent Neural Networks appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Introduction Hello! Today I am going to try my best in explaining.

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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. Optimization and Training: The routing algorithms in CapsNets can be challenging to optimize, requiring further research to improve training efficiency.

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Marktechpost

NOVEMBER 16, 2024

Over two weeks, you’ll learn to extract features from images, apply deep learning techniques for tasks like classification, and work on a real-world project to detect facial key points using a convolutional neural network (CNN). Key topics include CNNs, RNNs, SLAM, and object tracking.

Computer Vision 103

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Towards AI

FEBRUARY 21, 2023

Review: IDECNN-Improved Differential Evolution of Convolutional Neural Network (Image Classification) In this story, our research paper, titled: “Designing optimal convolutional neural network architecture using differential evolution algorithm” [1], is reviewed.

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Unite.AI

DECEMBER 26, 2024

AI algorithms can be trained on a dataset of countless scenarios, adding an advanced level of accuracy in differentiating between the activities of daily living and the trajectory of falls that necessitate concern or emergency intervention. Where does this data come from?

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Marktechpost

FEBRUARY 12, 2024

Deep convolutional neural networks (DCNNs) have been a game-changer for several computer vision tasks. As a result, many people are interested in finding ways to maximize the energy efficiency of DNNs through algorithm and hardware optimization. There are three notable characteristics of PDC in general.

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Marktechpost

JANUARY 9, 2024

It mentions the under-utilization of the Siamese neural network technique in recent studies on multimodal medical image classification, which motivates this study. TwinCNN combines a twin convolutional neural network framework with a hybrid binary optimizer for multimodal breast cancer digital image classification.

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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

JULY 23, 2024

Once the brain signals are collected, AI algorithms process the data to identify patterns. These algorithms map the detected patterns to specific thoughts, visual perceptions, or actions. These patterns are then decoded using deep neural networks to reconstruct the perceived images.

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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). Artificial Neural Networks are computational systems inspired by the human brain’s structure and functionality. How Do Artificial Neural Networks Work?

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Pickl AI

SEPTEMBER 5, 2024

Summary: Convolutional Neural Networks (CNNs) are essential deep learning algorithms for analysing visual data. Introduction Neural networks have revolutionised Artificial Intelligence by mimicking the human brai n’s structure to process complex data. What are Convolutional Neural Networks?

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Unite.AI

NOVEMBER 6, 2023

Traditional machine learning is a broad term that covers a wide variety of algorithms primarily driven by statistics. The two main types of traditional ML algorithms are supervised and unsupervised. These algorithms are designed to develop models from structured datasets. Do We Still Need Traditional Machine Learning Algorithms?

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AI Weekly

SEPTEMBER 12, 2024

techcrunch.com Algorithm sharpens facial features for better emotion detection New research introduces a method to improve the accuracy and speed of dynamic emotion recognition using a convolutional neural network (CNN) to analyze faces. Register now dotai.io update and beyond.

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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 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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Unite.AI

SEPTEMBER 21, 2023

Organizations and practitioners build AI models that are specialized algorithms to perform real-world tasks such as image classification, object detection, and natural language processing. Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models.

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AI Weekly

MARCH 21, 2024

nature.com Authorship attribution in twitter: a comparative study of machine learning and deep learning approaches This study proposes an AA approach using machine and deep learning algorithms to accurately predict the author of unknown posts on social media platforms.

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AssemblyAI

JANUARY 23, 2025

It works by analyzing audio signals, identifying patterns, and matching them to words and phrases using advanced algorithms. The primary drawbacks of cloud-based solutions are their cost and the lack of control over the underlying infrastructure and algorithms, as they are managed by the service provider.

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Marktechpost

OCTOBER 4, 2024

Evaluated Models Ready Tensor’s benchmarking study categorized the 25 evaluated models into three main types: Machine Learning (ML) models, Neural Network models, and a special category called the Distance Profile model. Prominent models include Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN).

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Flipboard

FEBRUARY 7, 2025

An AI algorithm may help identify breast cancer on MRI scans 1 year earlier than current methods allow, according to research published in Academic Radiology.Researchers trained the convolutional neural network AI model using MRI from 52,598 breasts.

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Becoming Human

MARCH 16, 2023

This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications. Through the next series of this post here , I will try to make an implementation of Graph Convolutional Neural Network. How do Graph Neural Networks work?

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Heartbeat

FEBRUARY 6, 2023

How do Object Detection Algorithms Work? There are two main categories of object detection algorithms. Two-Stage Algorithms: Two-stage object detection algorithms consist of two different stages. In the second step, these potential fields are classified and corrected by the neural network model.

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Unite.AI

AUGUST 1, 2023

OpenAI has been instrumental in developing revolutionary tools like the OpenAI Gym, designed for training reinforcement algorithms, and GPT-n models. Prompt 1 : “Tell me about Convolutional Neural Networks.” Unlike other neural networks, they leverage convolutional layers and pooling layers to process images.

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NVIDIA

OCTOBER 8, 2024

The review also emphasizes that the key tool for exploring these methods is GPU-accelerated simulation of quantum algorithms. The study focuses on how future quantum neural networks can use quantum computing to enhance existing AI techniques.

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Marktechpost

NOVEMBER 27, 2024

At its core, machine learning algorithms seek to identify patterns within data, enabling computers to learn and adapt to new information. 2) Logistic regression Logistic regression is a classification algorithm used to model the probability of a binary outcome. Sigmoid Kernel: Inspired by neural networks.

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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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Marktechpost

MAY 26, 2024

The rapid growth of AI and complex neural networks drives the need for efficient hardware that suits power and resource constraints. Designing and deploying these systems requires a comprehensive hardware-software co-design toolchain that optimizes across devices, circuits, and algorithms.

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Marktechpost

APRIL 12, 2024

This article explores some of the most influential deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformers, and Encoder-Decoder architectures, highlighting their unique features, applications, and how they compare against each other.

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Flipboard

NOVEMBER 5, 2024

It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC).

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Marktechpost

JANUARY 30, 2025

This paradigm shift is particularly visible in applications such as: Autonomous Vehicles Self-driving cars and drones rely on perception modules (sensors, cameras) fused with advanced algorithms to operate in dynamic traffic and weather conditions. Embeddings like word2vec, GloVe , or contextual embeddings from large language models (e.g.,

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ODSC - Open Data Science

NOVEMBER 28, 2024

Calculating Receptive Field for Convolutional Neural Networks Convolutional neural networks (CNNs) differ from conventional, fully connected neural networks (FCNNs) because they process information in distinct ways. Receptive fields are the backbone of CNN efficacy.

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Unite.AI

JANUARY 19, 2024

Traditionally, models for single-view object reconstruction built on convolutional neural networks have shown remarkable performance in reconstruction tasks. More recent depth estimation frameworks deploy convolutional neural network structures to extract depth in a monocular image.

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Marktechpost

MAY 5, 2024

Machine Learning with Python This course covers the fundamentals of machine learning algorithms and when to use each of them. The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. and evaluating the same.

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Unite.AI

AUGUST 2, 2023

To tackle the issue of single modality, Meta AI released the data2vec, the first of a kind, self supervised high-performance algorithm to learn patterns information from three different modalities: image, text, and speech. Why Does the AI Industry Need the Data2Vec Algorithm?

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IBM Journey to AI blog

MAY 6, 2024

Machine learning and deep neural network models can effectively analyze this data to identify patterns, correlations and relationships, which is particularly useful for understanding a patient’s unique profile. These algorithms can help curate highly personalized advertisements and content tailored to the desired audience.

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Marktechpost

SEPTEMBER 1, 2023

Incorporating machine learning and deep learning algorithms has shown promise in bolstering security. ’ The authors concluded that their extensive investigation into deep learning (DL) and machine learning (ML) algorithms in the context of biometric authentication yielded crucial insights.

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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. This means machine learning algorithms are used to analyze and cluster unlabeled datasets by discovering hidden patterns or data groups without the need for human intervention. How Does Image Classification Work?

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