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Mini-batch gradient descent in deep learning explained

Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of ...
Business Insider

WiMi Studies Hybrid Quantum-Classical Convolutional Neural Network Model

BEIJING, Oct. 23, 2025 (GLOBE NEWSWIRE) -- BEIJING, Oct. 23, 2025––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology ...
marktechpost

Privacy Implications and Comparisons of Batch Sampling Methods in Differentially Private ...

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for training machine learning models like neural networks while ensuring privacy. It modifies the standard gradient descent ...
Hacker

Extending Stochastic Gradient Optimization with ADAM

Gradient descent is a method to minimize an objective function F(θ) It’s like a “fitness tracker” for your model — it tells you how good or bad your model’’ predictions are. Gradient descent isn’t a ...
Scientific Research Publishing

Hinton, G., Srivastava, N. and Swersky, K. (2012) Neural Networks for Machine Learning ...

ABSTRACT: The development of artificial intelligence (AI), particularly deep learning, has made it possible to accelerate and improve the processing of data collected in different fields (commerce, ...
IEEE

Research on the Application of Gradient Descent Algorithm in Machine Learning

Abstract: The gradient descent algorithm is a type of optimization algorithm that is widely used to solve machine learning algorithm model parameters. Through continuous iteration, it obtains the ...