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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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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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

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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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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Analytics Vidhya

FEBRUARY 22, 2023

Introduction From the 2000s onward, Many convolutional neural networks have been emerging, trying to push the limits of their antecedents by applying state-of-the-art techniques. The ultimate goal of these deep learning algorithms is to mimic the human eye’s capacity to perceive the surrounding environment.

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Analytics Vidhya

DECEMBER 14, 2020

The post An Approach towards Neural Network based Image Clustering appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Introduction: Hi everyone, recently while participating in a Deep Learning competition, I.

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Analytics Vidhya

FEBRUARY 16, 2020

Overview Check out 3 different types of neural networks in deep learning Understand when to use which type of neural network for solving a. The post CNN vs. RNN vs. MLP – Analyzing 3 Types of Neural Networks in Deep Learning appeared first on Analytics Vidhya.

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Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

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Pickl AI

NOVEMBER 13, 2024

Summary: Backpropagation in neural network optimises models by adjusting weights to reduce errors. Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems.

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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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Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

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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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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

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.

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Bugra Akyildiz

FEBRUARY 3, 2024

SMAD traditionally relies on hand-crafted features and statistical models, but Netflix in this post how they leverage deep learning , particularly convolutional neural networks (CNNs) , for superior performance and generalizability. Libraries AutoRound is an advanced weight-only quantization algorithm, based on SignRound.

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Flipboard

NOVEMBER 14, 2024

In this paper, self-attention convolutional neural network based partial prior transfer learning (SACNN-PPTL) is proposed to improve the classification performance of patients’ MI multi-task. In the experiment, five backbone networks and three training modes are selected as comparison algorithms.

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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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Pickl AI

SEPTEMBER 15, 2024

Summary: Siamese Neural Networks use twin subnetworks to compare pairs of inputs and measure their similarity. Introduction Neural networks form the backbone of Deep Learning , allowing machines to learn from data by mimicking the human brain’s structure. What is a Siamese Neural Network?

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NYU Center for Data Science

JANUARY 31, 2024

The idea that neural networks undergo distinct developmental phases during training has long been a subject of debate and fascination. Published in the Transactions on Machine Learning Research (TMLR) , Hu’s work offers new insights into the training dynamics of neural networks.

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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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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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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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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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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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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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Heartbeat

MAY 2, 2023

Deep learning is based in algorithms, which are mathematical operations that let computers learn from data and get better over time. In this article, we will examine some different popular deep learning algorithms and how they operate. Each of the nodes gets an input, processes it, and produces an output.

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Viso.ai

OCTOBER 9, 2024

Human-machine interaction is an important area of research where machine learning algorithms with visual perception aim to gain an understanding of human interaction. State-of-the-art emotion AI Algorithms Outlook, current research, and applications What Is AI Emotion Recognition? About us: Viso.ai What is Emotion AI?

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Mlearning.ai

FEBRUARY 25, 2023

How does the Artificial Neural Network algorithm work? In the same way, artificial neural networks (ANNs) were developed inspired by neurons in the brain. ANN approach is a machine learning algorithm inspired by biological neural networks. Neural networks were trained faster with GPUs.

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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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Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. How Deep Learning Algorithms Work? The initial weights and biases of the neural network are randomly assigned.

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Unite.AI

OCTOBER 22, 2024

This term refers to how much time, memory, or processing power an algorithm requires as the size of the input grows. AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands.

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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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AI Weekly

JUNE 29, 2023

In the News Next DeepMind's Algorithm To Eclipse ChatGPT IN 2016, an AI program called AlphaGo from Google’s DeepMind AI lab made history by defeating a champion player of the board game Go. Powered by pluto.fi Try Pluto for free today] pluto.fi global investment arm, bringing the total capital raised to $165 million.

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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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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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Mlearning.ai

JULY 17, 2023

How taking inspiration from the brain can help us create Neural Networks. One of the most promising avenues for this kind of research is to learn from evolution and biological systems to improve the design of AI Models (after all Neural Networks were based on our brains).

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