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不可不读的机器学习面试宝典!微软全球执行副总裁、美国工程院院士沈向洋,《浪潮之巅》《数学之美》作者吴军,《计算广告》作者、科大讯飞副总裁刘鹏,联袂推荐!


人工智能几起几落,最近这次人工智能浪潮起始于近10年,技术的飞跃发展,带来了应用的可能性。未来的几年,是人工智能技术全面普及化的时期,也是算法工程师稀缺的时期。


本书旨在帮助对人工智能和机器学习有兴趣的朋友们更加深入地了解这个领域的基本技能,帮助每个软件工程师成为自信的AI实践者,帮助每个数据科学家成为出众的AI研究者。对每个有志进入此领域的工程师来说,本书会为你铺设一条快速通往目标的道路。


Content Description

人工智能领域正在以超乎人们想象的速度发展,本书赶在人工智能彻底占领世界之前完成编写,实属万幸。


书中收录了超过100道机器学习算法工程师的面试题目和解答,其中大部分源于Hulu算法研究岗位的真实场景。本书从日常工作、生活中各种有趣的现象出发,不仅囊括了机器学习的基本知识,而且还包含了成为出众算法工程师的相关技能,更重要的是凝聚了笔者对人工智能领域的一颗热忱之心,旨在培养读者发现问题、解决问题、扩展问题的能力,建立对机器学习的热爱,共绘人工智能世界的宏伟蓝图。


“不积跬步,无以至千里”,本书将从特征工程、模型评估、降维等经典机器学习领域出发,构建一个算法工程师必-备的知识体系;见神经网络、强化学习、生成对抗网络等新科研进展之微,知深度学习领域胜败兴衰之著;“博观而约取,厚积而薄发”,在末一章为读者展示生活中各种yin领时代的人工智能应用。


Author Description

诸葛越:现任Hulu公司全球研发副总裁,中国研发中心总经理。曾任Landscape Mobile 公司联合创始人兼CEO,前雅虎北京全球研发中心产品总监, 微软北京研发中心项目总经理,雅虎美国高级软件架构师。诸葛越获美国斯坦福大学的计算机硕士与博士,纽约州立大学石溪分校的应用数学硕士,曾就读于清华大学计算机科学与技术系。诸葛越的研究结果获多项专利,2005年获美国计算机学会数据库专业委员会十年z佳论文奖。


葫芦娃:15位Hulu北京创新实验室的人才。他们利用擅长的机器学习、深度学习等领域知识和算法模型,建立了一套定制化的机AI平台,改变着推荐引擎、视频编解码、内容理解、广告投放等多项与用户息息相关的在线业务技术。


Comments

诸葛越博士主编的此书是Hulu公司里十几位每天做前沿机器学习工作的工程师科学家一起合写的,是原创的、实际的、直接可用的内容。这本书致力于普及人工智能和机器学习,帮助每个软件工程师成为自信的AI实践者,每个数据科学家成为出众的AI研究者。

沈向洋/ 微软全球执行副总裁,美国工程院院士


计算机理论和算法常常让人感到高冷,因为它们和具体的应用之间缺少了一些桥梁。诸葛越博士的这本书,教授大家如何搭建这些桥梁。它可以让计算机的从业者对理论的认识有一个飞跃,也可以让非计算机专业的工程人员了解计算机科学这个强大的工具。

吴军/《浪潮之巅》《数学之美》作者


在越来越多的机器学习专业书籍中,Hulu工程师们的这本专著让我眼前一亮。本书没有拾人牙慧,从学术角度来整理机器学习的理论和算法体系;而是从一线工程师的视角出发,在职业方向、面试重点、模型实战和应用案例等诸方面有精到的介绍。对每个有志进入此领域的工程师来说,本书为你铺设了一条快速通往目标的道路。另外,本书为多位工业界实战专家合作完成,内容却相当连贯系统,实在难能可贵。

刘鹏/《计算广告》作者,科大讯飞副总裁


Catalogue

推荐序


前言


机器学习算法工程师的自我修养


第1章 特征工程


第1节 特征归一化


第2节 类别型特征


第3节高维组合特征的处理


第4节组合特征


第5节文本表示模型


第6节Word2Vec


第7节图像数据不足时的处理方法


第2章 模型评估


第1节 评估指标的局限性


第2节 ROC 曲线


第3节余弦距离的应用


第4节A/B 测试的陷阱


第5节模型评估的方法


第6节超参数调优


第7节过拟合与欠拟合


第3章经典算法


第1节 支持向量机


第2节 逻辑回归


第3节决策树


第4章降维


第1节 PCA 最大方差理论


第2节 PCA 最小平方误差理论


第3节线性判别分析


第4节线性判别分析与主成分分析


第5章非监督学习


第1节 K 均值聚类


第2节 高斯混合模型


第3节自组织映射神经网络


第4节非监督学习算法的评估


第6章概率图模型


第1节 概率图模型的联合概率分布


第2节 概率图表示


第3节生成式模型与判别式模型


第4节马尔可夫模型


第5节主题模型


第7章优化算法


第1节 有监督学习的损失函数


第2节 机器学习中的优化问题


第3节经典优化算法


第4节梯度验证


第5节随机梯度下降法


第6节随机梯度下降法的加速


第7节L1 正则化与稀疏性


第8章采样


第1节 采样的作用


第2节 均匀分布随机数


第3节常见的采样方法


第4节高斯分布的采样


第5节马尔科夫蒙特卡洛采样法


第6节贝叶斯网络的采样


第7节不均衡样本集的重采样


第9章前向神经网络


第1节 多层感知机与布尔函数


第2节 深度神经网络中的激活函数


第3节多层感知机的反向传播算法


第4节神经网络训练技巧


第5节深度卷积神经网络


第6节深度残差网络


第10章 循环神经网络


第1节 循环神经网络和卷积神经网络


第2节 循环神经网络的梯度消失问题


第3节循环神经网络中的激活函数


第4节长短期记忆网络


第5节Seq2Seq 模型


第6节注意力机制


第11章 强化学习


第1节 强化学习基础


第2节 视频游戏里的强化学习


第3节策略梯度


第4节探索与利用


第12章 集成学习


第1节 集成学习的种类


第2节 集成学习的步骤和例子


第3节基分类器


第4节偏差与方差


第5节梯度提升决策树的基本原理


第6节XGBoost 与GBDT 的联系和区别


第13章 生成式对抗网络


第1节 初识GANs 的秘密


第2节 WGAN:抓住低维的幽灵


第3节DCGAN:当GANs 遇上卷积


第4节ALI:包揽推断业务


第5节IRGAN:生成离散样本


第6节SeqGAN:生成文本序列


第14章 人工智能的热门应用


第1节 计算广告


第2节 游戏中的人工智能


第3节AI 在自动驾驶中的应用


第4节机器翻译


第5节人机交互中的智能计算


后记


作者随笔


参考文献



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