深度学习资源整合
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cover:"https://img2020.cnblogs.com/blog/1489774/202101/1489774-20210122003859601-1953857265.jpg",
title: "Python深度学习",
author: "François Chollet",
desc:
"本书由Keras之父、现任Google人工智能研究员的弗朗索瓦•肖莱(FrançoisChollet)执笔,详尽介绍了用Python和Keras进行深度学习的探索实践,包括计算机视觉、自然语言处理、产生式模型等应用。",
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title: "机器学习实战:基于Scikit-Learn、Keras和TensorFlow",
author:'Aurelien Geron 等',
desc:
"本书主要分为两个部分。第一部分为第1章到第8章,涵盖机器学习的基础理论知识和基本算法——从线性回归到随机森林等,帮助读者掌握Scikit-Learn的常用方法;第二部分为第9章到第16章,探讨深度学习和常用框架TensorFlow,一步一个脚印地带领读者使用TensorFlow搭建和训练深度神经网络,以及卷积神经网络。",
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title: "深度学习",
author:"Ian Goodfellow 等",
desc:
"《深度学习》由全球知名的三位专家Ian Goodfellow、Yoshua Bengio 和AaronCourville撰写, 是深度学习领域奠基性的经典教材。 全书的内容包括3个部分: 第1部分介绍基本的数学工具和机器学习的概念, 它们是深度学习的预备知识; 第2部分系统深入地讲解现今已成熟的深度学习方法和技术; 第3部分讨论某些具有前瞻性的方向和想法, 它们被公认为是深度学习未来的研究重点。 ",
douban: "https://book.douban.com/subject/27087503/",
pdf: "https://lanzous.com/icemt0h",
code: "https://gitee.com/baayso/deeplearningbook-chinese",
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title: "深度学习入门:基于Python的理论与实现",
author:"斋藤康毅",
desc:
"本书是深度学习真正意义上的入门书,深入浅出地剖析了深度学习的原理和相关技术。书中使用Python3,尽量不依赖外部库或工具,从基本的数学知识出发,带领读者从零创建一个经典的深度学习网络,使读者在此过程中逐步理解深度学习。",
douban: "https://book.douban.com/subject/30270959/",
pdf: "https://lanzous.com/icen46j",
code: "https://gitee.com/liuli217/deep-learning-from-scratch",
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title: "机器学习实战",
desc:
"本书第一部分主要介绍机器学习基础,以及如何利用算法进行分类,并逐步介绍了多种经典的监督学习算法,如k近邻算法、朴素贝叶斯算法、Logistic回归算法、支持向量机、AdaBoost集成方法、基于树的回归算法和分类回归树(CART)算法等。第三部分则重点介绍无监督学习及其一些主要算法:k均值聚类算法、Apriori算法、FP-Growth算法。第四部分介绍了机器学习算法的一些附属工具。",
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author:"Peter Harrington",
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video: "https://www.bilibili.com/video/BV16t411Q7TM",
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cover:"https://img2020.cnblogs.com/blog/1489774/202101/1489774-20210122004117121-239535574.jpg",
title: "Python深度学习:基于PyTorch",
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"本书是多位人工智能技术专家和大数据技术专家多年工作经验的结晶,从工具使用、技术原理、算法设计、案例实现等多个维度对深度学习进行了系统的讲解。内容选择上,广泛涉猎、重点突出、注重实战;内容安排上,实例切入、由浅入深、循序渐进;表达形式上,深度抽象、化繁为简、用图说话。",
douban: "https://book.douban.com/subject/34873001/",
author:"吴茂贵",
pdf: "https://wwa.lanzous.com/i9ydpiv5qte",
code:
"https://hub.fastgit.org/ZhangXinNan/DL-with-Python-and-PyTorch",
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title: "机器学习(西瓜书)",
author:"周志华",
desc:
"机器学习是计算机科学与人工智能的重要分支领域. 本书作为该领域的入门教材,在内容上尽可能涵盖机器学习基础知识的各方面。 为了使尽可能多的读者通过本书对机器学习有所了解, 作者试图尽可能少地使用数学知识. 然而, 少量的概率、统计、代数、优化、逻辑知识似乎不可避免. 因此, 本书更适合大学三年级以上的理工科本科生和研究生, 以及具有类似背景的对机器学 习感兴趣的人士. ",
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title: "统计学习方法",
author:"李航",
desc:
"详细介绍支持向量机、Boosting、最大熵、条件随机场等十个统计学习方法。 ",
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