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+ [深度学习秘籍书](README.md) | ||
+ [前言](dl-cb_00.md) | ||
+ [第一章:工具和技术](dl-cb_01.md) | ||
+ [第二章:摆脱困境](dl-cb_02.md) | ||
+ [第三章:使用单词嵌入计算文本相似性](dl-cb_03.md) | ||
+ [第四章:基于维基百科外部链接构建推荐系统](dl-cb_04.md) | ||
+ [第五章:生成类似示例文本风格的文本](dl-cb_05.md) | ||
+ [第六章:问题匹配](dl-cb_06.md) | ||
+ [第七章:建议表情符号](dl-cb_07.md) | ||
+ [第八章:序列到序列映射](dl-cb_08.md) | ||
+ [第九章:重用预训练的图像识别网络](dl-cb_09.md) | ||
+ [第十章:构建反向图像搜索服务](dl-cb_10.md) | ||
+ [第十一章:检测多个图像](dl-cb_11.md) | ||
+ [第十二章:图像风格](dl-cb_12.md) | ||
+ [第十三章:使用自动编码器生成图像](dl-cb_13.md) | ||
+ [第十四章:使用深度网络生成图标](dl-cb_14.md) | ||
+ [第十五章:音乐和深度学习](dl-cb_15.md) | ||
+ [第十六章:生产机器学习系统](dl-cb_16.md) |
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+ [面向金融的深度学习(提前发布)](README.md) | ||
+ [第一章:介绍数据科学和交易](dl-fin_0.md) | ||
+ [第二章:深度学习的基本概率方法](dl-fin_1.md) | ||
+ [第三章:描述性统计和数据分析](dl-fin_2.md) | ||
+ [第四章:深度学习的线性代数和微积分](dl-fin_3.md) | ||
+ [第五章:介绍技术分析](dl-fin_4.md) | ||
+ [第六章:数据科学的 Python 入门](dl-fin_5.md) | ||
+ [关于作者](dl-fin_6.md) |
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+ [面向生命科学的深度学习](README.md) | ||
+ [前言](dl-lfsci_00.md) | ||
+ [第一章:为什么选择生命科学?](dl-lfsci_01.md) | ||
+ [第二章:深度学习简介](dl-lfsci_02.md) | ||
+ [第三章:使用 DeepChem 进行机器学习](dl-lfsci_03.md) | ||
+ [分子是复杂的实体,研究人员已经开发了许多不同的技术来对其进行特征化。这些表示包括化学描述符向量,2D 图表示,3D 静电网格表示,轨道基函数表示等等。](dl-lfsci_04.md) | ||
+ [第五章:生物物理机器学习](dl-lfsci_05.md) | ||
+ [第六章:基因组学的深度学习](dl-lfsci_06.md) | ||
+ [第七章:机器学习用于显微镜](dl-lfsci_07.md) | ||
+ [第八章:医学的深度学习](dl-lfsci_08.md) | ||
+ [第九章:生成模型](dl-lfsci_09.md) | ||
+ [第十章:深度模型的解释](dl-lfsci_10.md) | ||
+ [第十一章:虚拟筛选工作流程示例](dl-lfsci_11.md) | ||
+ [第十二章:前景与展望](dl-lfsci_12.md) |
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+ [从零开始的深度学习](README.md) | ||
+ [前言](dl-scr_0.md) | ||
+ [第一章:基础](dl-scr_1.md) | ||
+ [第二章:基础知识](dl-scr_2.md) | ||
+ [第三章:从头开始的深度学习](dl-scr_3.md) | ||
+ [第四章:扩展](dl-scr_4.md) | ||
+ [第五章:卷积神经网络](dl-scr_5.md) | ||
+ [第六章:循环神经网络](dl-scr_6.md) | ||
+ [第七章:PyTorch](dl-scr_7.md) | ||
+ [附录 A. 深入探讨](dl-scr_8.md) |
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+ [深度学习基础第二版](README.md) | ||
+ [前言](fund-dl_00.md) | ||
+ [第一章:深度学习的线性代数基础](fund-dl_01.md) | ||
+ [第二章:概率基础](fund-dl_02.md) | ||
+ [第三章:神经网络](fund-dl_03.md) | ||
+ [第四章:训练前馈神经网络](fund-dl_04.md) | ||
+ [第五章:在 PyTorch 中实现神经网络](fund-dl_05.md) | ||
+ [第六章:超越梯度下降](fund-dl_06.md) | ||
+ [第七章:卷积神经网络](fund-dl_07.md) | ||
+ [第八章:嵌入和表示学习](fund-dl_08.md) | ||
+ [第九章:序列分析模型](fund-dl_09.md) | ||
+ [第十章:生成模型](fund-dl_10.md) | ||
+ [第十一章:可解释性方法](fund-dl_11.md) | ||
+ [第十二章:记忆增强神经网络](fund-dl_12.md) | ||
+ [第十三章:深度强化学习](fund-dl_13.md) |
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+ [生成式深度学习](README.md) | ||
+ [前言](gen-dl_00.md) | ||
+ [前言](gen-dl_01.md) | ||
+ [第一部分:生成式深度学习简介](gen-dl_02.md) | ||
+ [第一章:生成建模](gen-dl_03.md) | ||
+ [第二章:深度学习](gen-dl_04.md) | ||
+ [第二部分:方法](gen-dl_05.md) | ||
+ [第三章:变分自动编码器](gen-dl_06.md) | ||
+ [第四章:生成对抗网络](gen-dl_07.md) | ||
+ [第五章:自回归模型](gen-dl_08.md) | ||
+ [第六章:正规化流模型](gen-dl_09.md) | ||
+ [第七章:基于能量的模型](gen-dl_10.md) | ||
+ [第八章:扩散模型](gen-dl_11.md) | ||
+ [第三部分:应用](gen-dl_12.md) | ||
+ [第九章:Transformer](gen-dl_13.md) | ||
+ [第十章:高级 GANs](gen-dl_14.md) | ||
+ [第十一章:音乐生成](gen-dl_15.md) | ||
+ [第十二章:世界模型](gen-dl_16.md) | ||
+ [第十三章:多模态模型](gen-dl_17.md) | ||
+ [第十四章:结论](gen-dl_18.md) |
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+ [面向云边端的深度学习实践指南](README.md) | ||
+ [前言](prac-dl-cld_00.md) | ||
+ [第一章:探索人工智能的领域](prac-dl-cld_01.md) | ||
+ [第二章:图片中有什么:使用 Keras 进行图像分类](prac-dl-cld_02.md) | ||
+ [第三章:猫与狗:使用 Keras 中的 30 行进行迁移学习](prac-dl-cld_03.md) | ||
+ [第四章:构建反向图像搜索引擎:理解嵌入](prac-dl-cld_04.md) | ||
+ [第五章:从新手到大师预测者:最大化卷积神经网络准确性](prac-dl-cld_05.md) | ||
+ [第六章:最大化 TensorFlow 的速度和性能:一个便捷清单](prac-dl-cld_06.md) | ||
+ [第七章:实用工具、技巧和窍门](prac-dl-cld_07.md) | ||
+ [第八章:云计算机视觉 API:15 分钟内上手](prac-dl-cld_08.md) | ||
+ [第九章:使用 TensorFlow Serving 和 KubeFlow 在云上进行可扩展推断服务](prac-dl-cld_09.md) | ||
+ [第十章:在浏览器中使用 TensorFlow.js 和 ml5.js 的人工智能](prac-dl-cld_10.md) | ||
+ [第十一章:在 iOS 上使用 Core ML 进行实时对象分类](prac-dl-cld_11.md) | ||
+ [第十二章:在 iOS 上使用 Core ML 和 Create ML 的 Not Hotdog](prac-dl-cld_12.md) | ||
+ [第十三章:食物的 Shazam:使用 TensorFlow Lite 和 ML Kit 开发 Android 应用程序](prac-dl-cld_13.md) | ||
+ [第十四章:使用 TensorFlow Object Detection API 构建完美的猫定位器应用](prac-dl-cld_14.md) | ||
+ [交并比](prac-dl-cld_15.md) | ||
+ [第十五章:成为创客:探索边缘嵌入式 AI](prac-dl-cld_16.md) | ||
+ [第十六章:使用 Keras 进行端到端深度学习模拟自动驾驶汽车](prac-dl-cld_17.md) | ||
+ [第十七章:在不到一个小时内构建自动驾驶汽车:使用 AWS DeepRacer 进行强化学习](prac-dl-cld_18.md) |