CHOIR
发表于 2025-3-23 12:16:15
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细胞膜
发表于 2025-3-23 14:58:27
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ODIUM
发表于 2025-3-23 21:14:21
Incorporating Artificial IntelligenceThis chapter starts by outlining the historical development trends of AI and then explains what deep learning is and how it performs in practical applications. The chapter concludes by briefly describing the features of MindSpore, Huawei‘s self-developed deep learning framework.
NICE
发表于 2025-3-23 22:47:14
Designing Human-Centric AI ExperiencesThis chapter describes several commonly used algorithms and basic concepts related to deep learning.
enormous
发表于 2025-3-24 05:20:36
Working Effectively with AI Tech TeamsThis chapter introduces several important concepts related to DNN and presents some examples of using MindSpore to implement simple neural networks.
patriot
发表于 2025-3-24 08:48:09
Popular Fallacies about HypertextThis chapter starts by describing the main challenges that face deep learning systems. It then explores the fundamentals involved in the training of DNNs, and concludes with some examples of using MindSpore to implement DNNs.
audiologist
发表于 2025-3-24 12:30:27
Armando J. Oliveira,Duarte Costa PereiraIn this chapter, we describe the CNN. This network is a special neural network that uses convolution instead of general matrix multiplication at one or more layers. In essence, it is a feedforward neural network that uses convolutional mathematical operations.
extinct
发表于 2025-3-24 17:14:07
Terry Mayes,Mike Kibby,Tony AndersonCalculations performed in the CNN are independent, meaning that there is no relationship between the previous and current inputs.
地名表
发表于 2025-3-24 21:02:38
Studies in Systems, Decision and ControlVariational autoencoders (VAEs), proposed by Kingma and Welling in 2013, allow us to design complex generative models of data, which can then be trained to generate fictional images such as celebrity faces and high-resolution digital artworks.
暗语
发表于 2025-3-25 00:06:35
Guidong Zhang,Bo Zhang,Zhong LiThis chapter starts by covering the basic concepts involved in reinforcement learning and then describes how to solve reinforcement learning tasks by using basic and deep learning-based solutions. It also provides a brief overview of the typical algorithms central to the deep learning-based solutions, namely DQN, DDPG, and A3C.