Aug 7, 2023 · This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing.
May 19, 2023 · This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing.
This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different ...
Nov 12, 2024 · This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing.
People also ask
What is a deep neural network in deep learning?
What is the biggest problem with neural networks?
What is the difference between CNN and DNN?
What is the role of neural networks in cloud computing?
This research presents an updated analysis of the most recent DNNs used in cloud computing. It highlights the necessity of cloud computing while presenting and ...
Mar 31, 2021 · We review current challenges (limitations) of Deep Learning including lack of training data, Imbalanced Data, Interpretability of data, ...
This paper explores the application of Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), to these critical issues. We employ the MNIST ...
This study summarizes the deep learning techniques into supervised, unsupervised, reinforcement, and hybrid learning-based models.
May 2, 2024 · However, challenges such as data privacy, scalability, and explainability, among others, are also identified as challenges of using machine ...
Jun 3, 2024 · It also provides insight into deep learning models and methodologies, as well as the challenges faced and the expected future course of deep ...