Items related to 深度学习(deep learning)

深度学习(deep learning) - Softcover

 
9787115461476: 深度学习(deep learning)

Synopsis

《深度学习》由全球知名的三位专家IanGoodfellow、YoshuaBengio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。《深度学习》适合各类读者阅读,包括相关专业的大学生或研究生,以及不具有机器学习或统计背景、但是想要快速补充深度学习知识,以便在实际产品或平台中应用的软件工程师。AI圣经!深度学习领域奠基性的经典畅销书!长期位居美国ya马逊AI和机器学习类图书榜首!所有数据科学家和机器学习从业者的bi读图书!特斯拉CEO埃隆·马斯克等国内外众多专家推jian!深度学习是机器学习的一个分支,它能够使计算机通过层次概念来学习经验和理解世界。因为计算机能够从经验中获取知识,所以不需要人类来形式化地定义计算机需要的所有知识。层次概念允许计算机通过构造简单的概念来学习复杂的概念,而这些分层的图结构将具有很深的层次。本书会介绍深度学习领域的许多主题。本书囊括了数学及相关概念的背景知识,包括线性代数、概率论、信息论、数值优化以及机器学习中的相关内容。同时,它还介绍了工业界中实践者用到的深度学习技术,包括深度前馈网络、正则化、优化算法、卷积网络、序列建模和实践方法等,并且调研了诸如自然语言处理、语音识别、计算机视觉、在线推荐系统、生物信息学以及视频游戏方面的应用。最后,本书还提供了一些研究方向,涵盖的理论主题包括线性因子模型、自编码器、表示学习、结构化概率模型、蒙特卡罗方法、配分函数、近似推断以及深度生成模型。封面特色:由艺术家DanielAmbrosi提供的中央公园杜鹃花步道梦幻景观。在Ambrosi的亿级像素全景图上,应用JosephSmarr(Google)和ChirsLamb(NVIDIA)修改后的GoogleDeepDream开源程序,创造了DanielAmbrosi的"幻景"。第1章引言111本书面向的读者712深度学习的历史趋势8121神经网络的众多名称和命运变迁8122与日俱增的数据量12123与日俱增的模型规模13124与日俱增的精度、复杂度和对现实世界的冲击15第1部分应用数学与机器学习基础第2章线性代数1921标量、向量、矩阵和张量1922矩阵和向量相乘2123单位矩阵和逆矩阵2224线性相关和生成子空间2325范数2426特殊类型的矩阵和向量2527特征分解2628奇异值分解2829Moore-Penrose伪逆28210迹运算29211行列式30212实例:主成分分析30第3章概率与信息论3431为什么要使用概率3432随机变量3533概率分布36331离散型变量和概率质量函数36332连续型变量和概率密度函数3634边缘概率3735条件概率3736条件概率的链式法则3837独立性和条件独立性3838期望、方差和协方差3839常用概率分布39391Bernoulli分布40392Multinoulli分布40393高斯分布40394指数分布和Laplac

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  • Publisher人民邮电出版社
  • Publication date2017
  • ISBN 10 7115461473
  • ISBN 13 9787115461476
  • BindingPaperback
  • LanguageChinese

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paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2017-08-01 Publisher: People's post and telecommunications publishing house deep Learning by the world-renowned three experts iangoodfellow. Yoshuabengio and Aaroncourville writing. It is the classic textbook of the Deep learning field. The contents of the book include 3 parts: Part 1th introduces basic math . Seller Inventory # DP043203

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