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Calculating Receptive Field for Convolutional Neural Networks

ODSC - Open Data Science

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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Unraveling Transformer Optimization: A Hessian-Based Explanation for Adam’s Superiority over SGD

Marktechpost

While the Adam optimizer has become the standard for training Transformers, stochastic gradient descent with momentum (SGD), which is highly effective for convolutional neural networks (CNNs), performs worse on Transformer models. This Magazine/Report will be released in late October/early November 2024.

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Understanding Graph Neural Network with hands-on example| Part-1

Becoming Human

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. So, let’s get started! What are Graphs?

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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

The two most common types of supervised learning are classification , where the algorithm predicts a categorical label, and regression , where the algorithm predicts a numerical value. Unsupervised Learning In this type of learning, the algorithm is trained on an unlabeled dataset, where no correct output is provided.

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The 11 Top AI Influencers to Watch in 2024 (Guide)

Viso.ai

From the development of sophisticated object detection algorithms to the rise of convolutional neural networks (CNNs) for image classification to innovations in facial recognition technology, applications of computer vision are transforming entire industries.

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Major trends in NLP: a review of 20 years of ACL research

NLP People

As the following chart shows, research activity has been flourishing in the past years: Figure 1: Paper quantity published at the ACL conference by years In the following, we summarize some core trends in terms of data strategies, algorithms, tasks as well as multilingual NLP. Neural Networks are the workhorse of Deep Learning (cf.

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