Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-lang...
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.
作者简介
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François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences i...
François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.
目录
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PART 1 - FUNDAMENTALS OF DEEP LEARNING
1.What is deep learning?
2.Before we begin: the mathematical building blocks of neural networks
3.Getting started with neural networks
4.Fundamentals of machine learning
PART 2 - DEEP LEARNING IN PRACTICE
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PART 1 - FUNDAMENTALS OF DEEP LEARNING
1.What is deep learning?
2.Before we begin: the mathematical building blocks of neural networks
3.Getting started with neural networks
4.Fundamentals of machine learning
PART 2 - DEEP LEARNING IN PRACTICE
5.Deep learning for computer vision
6.Deep learning for text and sequences
7.Advanced deep-learning best practices
8.Generative deep learning
9.Conclusions
appendix A - Installing Keras and its dependencies on Ubuntu
appendix B - Running Jupyter notebooks on an EC2 GPU instance
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本书自出版以来收到众多好评，因为是 Keras 作者写的书，所以全书基本围绕着 Keras 讲深度学习的各种实现，从 CNN，RNN到GAN等等，总体偏入门，但也承载着很多作者对深度学习整体性的思考。值得一提的是作者推荐用GPU 跑书中的例子，这不是开玩笑， 用CPU 跑你会感到很绝望的～...
(展开)
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1 有用 Momo June 2020-03-10 02:42:38
比较入门 理论非常浅 建议配合其他书籍加深理解 新手的话还是很值得上手把代码都敲一遍的
16 有用 齐木楠雄 2018-07-25 19:46:11
深度学习交给你的不仅仅是技术，还有人是如何认识事物的思想，而这也是与哲学更为接近的一部分，也是更为有趣的地方。越来越发现自然科学与哲学之间存在的千丝万缕联系是如此有趣，这也是我喜欢机器学习、模式识别的原因，超越技术之外的思想可以泛化到对世界的认知，而对“认知”过程和机理的了解则开启了最为奇妙的认知旅程——认识自己，则认识世界。在看过《形而上学》之后，看康德会有不一样的体会，而这些书应该也会为我看《... 深度学习交给你的不仅仅是技术，还有人是如何认识事物的思想，而这也是与哲学更为接近的一部分，也是更为有趣的地方。越来越发现自然科学与哲学之间存在的千丝万缕联系是如此有趣，这也是我喜欢机器学习、模式识别的原因，超越技术之外的思想可以泛化到对世界的认知，而对“认知”过程和机理的了解则开启了最为奇妙的认知旅程——认识自己，则认识世界。在看过《形而上学》之后，看康德会有不一样的体会，而这些书应该也会为我看《GEB》奠定很好的基础吧。 (展开)
1 有用 Nova 2019-01-29 11:14:14
之前读过本书作者的 blog 文章 User experience design for APIs，明白他能把很复杂的问题简明扼要地讲清楚，这本书也不例外，把很多道理讲透了。适合初学者入门，也适合入门者回顾基础知识。
0 有用 安分老王 2019-05-15 06:34:52
keras之父的书，简洁易懂
4 有用 o` 2019-01-03 23:03:08
Keras 作者写的书，光这一点就值得看了。抽象程度刚刚好，既没有陷入底层数学细节，也不像 fast.ai 那样过于“上层”。作者在书中各处融入了自己对机器学习的理解，让读者形成良好的直觉，堪称完美的入门书籍。