我附议推荐 Peter Bloem 的教程。
我也正准备读 Simon Prince 这本即将出版的优秀著作中的 transformer 章节：
udlbook https://udlbook.github.io/udlbook/
I second the recommendation for Peter Bloem’s tutorial.
I’m also about to read the transformer chapter from this excellent upcoming book by Simon Prince:
udlbook https://udlbook.github.io/udlbook/
我觉得《理解深度学习》（Understanding Deep Learning）非常好——https://udlbook.github.io/udlbook/（几乎涵盖了所有主题，可能只有少数几个遗漏，比如多模态学习、NERF 和时间序列预测）
I think "Understanding Deep Learning" is very nice - https://udlbook.github.io/udlbook/ (an covers almost all topics, it has maybe just a couple of omissions, such as Multimodal Learning, NERFs and Time Series Prediction)
《理解深度学习》（Understanding Deep Learning）这本书也涵盖了更新的模型：https://udlbook.github.io/udlbook/（有免费 PDF 和 Jupyter notebook）
The "Understanding Deep Learning" book covers more recent models as well: https://udlbook.github.io/udlbook/ (free PDF and Jupyter notebooks available)
这里列了很多好资源。但我觉得列表 [0] 里少了《理解深度学习》（Understanding Deep Learning）。在我看来，Simon J.D. Prince 这本书真是一项壮举，不仅材料本身，还有每章附的笔记，直接链接到进阶参考文献（免费的文献综述），以及真正挑战你对材料理解的习题……
Lots of great resources listed here. But I think there is "Understanding Deep Learning" missing from the list [0]. In my opinion, Simon J.D. Prince accomplished a true feat with his book, not only through the material itself but also with the notes attached to each chapter, linking directly to advanced references (free literature review), exercises that really challenge your understanding of the m
https://udlbook.github.io/udlbook/
This is a good beginner resource that goes over broad concepts with Jupyter Notebook examples, once you get through this I recommend looking through forums for more advanced topics that appeal to you
我附议推荐 Peter Bloem 的教程。 我也正准备读 Simon Prince 这本即将出版的优秀著作中的 transformer 章节： udlbook https://udlbook.github.io/udlbook/
I second the recommendation for Peter Bloem’s tutorial. I’m also about to read the transformer chapter from this excellent upcoming book by Simon Prince: udlbook https://udlbook.github.io/udlbook/
我觉得《理解深度学习》（Understanding Deep Learning）非常好——https://udlbook.github.io/udlbook/（几乎涵盖了所有主题，可能只有少数几个遗漏，比如多模态学习、NERF 和时间序列预测）
I think "Understanding Deep Learning" is very nice - https://udlbook.github.io/udlbook/ (an covers almost all topics, it has maybe just a couple of omissions, such as Multimodal Learning, NERFs and Time Series Prediction)
取决于你的数学背景，我会选 Bishop 的 Deep Learning Foundations and Concepts 和 Simon Prince 的《理解深度学习》（Understanding Deep Learning）。
Depending on your math background, i would go for Bishop's Deep Learning Foundations and Concepts and Simon Prince's Understanding Deep Learning.
《理解深度学习》（Understanding Deep Learning）这本书也涵盖了更新的模型：https://udlbook.github.io/udlbook/（有免费 PDF 和 Jupyter notebook）
The "Understanding Deep Learning" book covers more recent models as well: https://udlbook.github.io/udlbook/ (free PDF and Jupyter notebooks available)
这里列了很多好资源。但我觉得列表 [0] 里少了《理解深度学习》（Understanding Deep Learning）。在我看来，Simon J.D. Prince 这本书真是一项壮举，不仅材料本身，还有每章附的笔记，直接链接到进阶参考文献（免费的文献综述），以及真正挑战你对材料理解的习题……
Lots of great resources listed here. But I think there is "Understanding Deep Learning" missing from the list [0]. In my opinion, Simon J.D. Prince accomplished a true feat with his book, not only through the material itself but also with the notes attached to each chapter, linking directly to advanced references (free literature review), exercises that really challenge your understanding of the m
https://udlbook.github.io/udlbook/ 这是很好的入门资源，用 Jupyter Notebook 例子讲广泛的概念，读完这个之后，我建议去论坛翻翻你感兴趣的更进阶的话题
https://udlbook.github.io/udlbook/ This is a good beginner resource that goes over broad concepts with Jupyter Notebook examples, once you get through this I recommend looking through forums for more advanced topics that appeal to you