双注意力视觉Transformers (DaViT) 交替使用两种类型的Transformers:一种让图像小块互相关注,采用所有通道进行自注意力计算;另一种则是通道互相关注,采用所有图像小块进行自注意力计算。这种架构在ImageNet上的顶级错误率达到了9.60%,接近于撰稿时的最先进水平。 图12.18 移动窗口(SWin)Transformer(Liu et al., 202...
《Understanding Deep Learning》是由 Simon J.D. Prince 编写的一本关于深度学习的专业书籍,内容涵盖深度学习的理论基础、性能评估、卷积网络、Transformers、图神经网络、生成对抗网络(GANs)、扩散模型(Diffusion Models)、强化学习等主题,并附有大量练习题。
Chapter 11 - 残差网络 Residual networks and BatchNorm Chapter 12 - Transformers Chapter 13 - 图神经网络 Graph neural networks Chapter 14 - 无监督学习 Unsupervised learning Chapter 15 - 生成对抗网络 Generative adversarial networks Chapter 16 - Normalizing flows Chapter 17- 变分自编码器 Variational aut...
Chapter 12 Transformers.md 1314 Mar 13, 2024 Chapter 13 Graph neural networks.md 1314 Mar 13, 2024 Chapter 14 Unsupervised learning.md 1314 Mar 13, 2024 Chapter 15 Generative Adversarial Networks.md 15 Mar 17, 2024 Chapter 16 Normalizing flows.md 16 Mar 19, 2024 Chapter 17 Variational auto...
1.1 Supervised learning Figure 1.1 Machine learning is an area of artificial intelligence that fits mathematical models to observed data. It can coarsely be divided into supervised learning, unsupervised learning, and reinforcement learning. Deep neural networks contribute to each of these areas. ...
• Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such astransformersanddiffusion models • Short, focused chapters progress in complexity, easing students into difficult concepts • Pragmatic approach straddling theory and practice gives readers the ...
内容包括监督学习、神经网络、卷积网络、Transformers、扩散模型、强化学习等。 英文版和中文版。 下载权限 游客: ￥0.6 普通用户组: 50 VIP用户组: 免费下载 深入理解深度学习 (Understanding Deep Learning) (Simon J.D. Prince) (pdf) 文件大小: 35.3MB 文件格式: pdf 出版发行: The MIT Press 出版时间: ...
Deep learning is also a building block to GenAI: Large language models (LLMs), a variety of GenAI, are a type of transformer network, and transformers are a specialized deep learning architecture. In simpler terms, LLMs are algorithms that employ deep learning methods. Hence, deep learning ...
该书是由 Simon J.D. Prince 编写的一本关于深度学习的专业书籍,内容涵盖深度学习的理论基础、性能评估、卷积网络、Transformers、图神经网络、生成对抗网络(GANs)、扩散模型(Diffusion Models)、强化学习等主题,并附有大量练习题。 收录于: 第103 期 标签: 深度学习 书籍 AI...
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and ri...