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...
AI爱好者必看,MIT出版《理解深度学习》 Understanding Deep Learning这个项目,目前已经有中文版开始更新,如果你想要深入理解深度学习的奥秘,那么一定不要错过,建议先点赞收藏。这本书深入讲解了深度学习的核心概念,总 - AI探长于20240805发布在抖音,已经收获了52.4万
• Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models • Short, focused chapters progress in complexity, easing students into difficult concepts • Pragmatic approach straddling theory and practice gives readers t...
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 ...
and initializationChapter 8 - 衡量性能 Measuring performanceChapter 9 - 正则化 RegularizationChapter 10 - 卷积网络 Convolutional netsChapter 11 - 残差网络 Residual networks and BatchNormChapter 12 - TransformersChapter 13 - 图神经网络 Graph neural networksChapter 14 - 无监督学习Unsupervised learning...
Chapter 12 Transformers.md Chapter 13 Graph neural networks.md Chapter 14 Unsupervised learning.md Chapter 15 Generative Adversarial Networks.md Chapter 16 Normalizing flows.md Chapter 17 Variational autoencoders.md Chapter 18 Diffusion Models.md ...
Chapter 12 Transformers.md Chapter 13 Graph neural networks.md Chapter 14 Unsupervised learning.md Chapter 15 Generative Adversarial Networks.md Chapter 16 Normalizing flows.md Chapter 17 Variational autoencoders.md Chapter 18 Diffusion Models.md ...
Transformers have become the defacto standard for any NLP tasks nowadays. Not only that, but they are now also being used in Computer Vision and to generate music. I am sure you would all have heard about the GPT3 Transformer and its applications thereof.But all these things aside, they ...
It enabled further development in the field by paving the road forTransformersand SOTA models likeGoogle’s BERT— which inspiredRoBERTaby Facebook, AzureML-BERT by Microsoft and many more. Conclusion In our current approach with RNNs, the intermediary state act as a bottleneck for performance. ...
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 ...