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Title: GitHub - lizhengjava/deeplearningbook-chinese: Deep Learning Book Chinese Translation

Open Graph Title: GitHub - lizhengjava/deeplearningbook-chinese: Deep Learning Book Chinese Translation

X Title: GitHub - lizhengjava/deeplearningbook-chinese: Deep Learning Book Chinese Translation

Description: Deep Learning Book Chinese Translation. Contribute to lizhengjava/deeplearningbook-chinese development by creating an account on GitHub.

Open Graph Description: Deep Learning Book Chinese Translation. Contribute to lizhengjava/deeplearningbook-chinese development by creating an account on GitHub.

X Description: Deep Learning Book Chinese Translation. Contribute to lizhengjava/deeplearningbook-chinese development by creating an account on GitHub.

Opengraph URL: https://github.com/lizhengjava/deeplearningbook-chinese

X: @github

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https://patch-diff.githubusercontent.com/lizhengjava/deeplearningbook-chinese#面向的读者
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https://patch-diff.githubusercontent.com/lizhengjava/deeplearningbook-chinese#出版及开源原因
地址http://www.epubit.com.cn/book/details/4278
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https://patch-diff.githubusercontent.com/lizhengjava/deeplearningbook-chinese#致谢
第一章 前言https://exacity.github.io/deeplearningbook-chinese/Chapter1_introduction/
第二章 线性代数https://exacity.github.io/deeplearningbook-chinese/Chapter2_linear_algebra/
第三章 概率与信息论https://exacity.github.io/deeplearningbook-chinese/Chapter3_probability_and_information_theory/
第四章 数值计算https://exacity.github.io/deeplearningbook-chinese/Chapter4_numerical_computation/
第五章 机器学习基础https://exacity.github.io/deeplearningbook-chinese/Chapter5_machine_learning_basics/
第六章 深度前馈网络https://exacity.github.io/deeplearningbook-chinese/Chapter6_deep_feedforward_networks/
第七章 深度学习中的正则化https://exacity.github.io/deeplearningbook-chinese/Chapter7_regularization/
第八章 深度模型中的优化https://exacity.github.io/deeplearningbook-chinese/Chapter8_optimization_for_training_deep_models/
第九章 卷积网络https://exacity.github.io/deeplearningbook-chinese/Chapter9_convolutional_networks/
第十章 序列建模:循环和递归网络https://exacity.github.io/deeplearningbook-chinese/Chapter10_sequence_modeling_rnn/
第十一章 实践方法论https://exacity.github.io/deeplearningbook-chinese/Chapter11_practical_methodology/
第十二章 应用https://exacity.github.io/deeplearningbook-chinese/Chapter12_applications/
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第十六章 深度学习中的结构化概率模型https://exacity.github.io/deeplearningbook-chinese/Chapter16_structured_probabilistic_modelling/
第十七章 蒙特卡罗方法https://exacity.github.io/deeplearningbook-chinese/Chapter17_monte_carlo_methods/
第十八章 面对配分函数https://exacity.github.io/deeplearningbook-chinese/Chapter18_confronting_the_partition_function/
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