Neural Networks and Deep Learning: A Textbook

Charu C. Aggarwal

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商品描述

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book  is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories:

The basics of neural networks:  Many traditional machine learning models can be understood as special cases of neural networks.  An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec.

 

Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.

Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10.

 

The book is written for graduate students, researchers, and practitioners.   Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

 

商品描述(中文翻譯)

本書涵蓋了深度學習中的經典模型和現代模型。主要關注深度學習的理論和算法。神經網絡的理論和算法對於理解重要概念非常重要,以便理解不同應用中神經架構的重要設計概念。為什麼神經網絡有效?什麼時候它們比現成的機器學習模型更好?深度何時有用?為什麼訓練神經網絡如此困難?有哪些陷阱?本書還豐富地討論了不同應用,以使從業人員了解神經架構如何為不同類型的問題設計。涵蓋了與推薦系統、機器翻譯、圖像標題生成、圖像分類、基於強化學習的遊戲和文本分析等許多不同領域相關的應用。本書的章節分為三個類別:

神經網絡的基礎:許多傳統機器學習模型可以理解為神經網絡的特殊情況。前兩章強調理解傳統機器學習和神經網絡之間的關係。支持向量機、線性/邏輯回歸、奇異值分解、矩陣分解和推薦系統被證明是神經網絡的特殊情況。這些方法與最近的特徵工程方法(如word2vec)一起研究。

神經網絡的基礎知識:第3章和第4章詳細討論了訓練和正則化。第5章和第6章介紹了基於徑向基函數(RBF)的網絡和受限玻爾茨曼機。

神經網絡的高級主題:第7章和第8章討論了循環神經網絡和卷積神經網絡。第9章和第10章介紹了深度強化學習、神經圖靈機、Kohonen自組織映射和生成對抗網絡等幾個高級主題。

本書針對研究生、研究人員和從業人員撰寫。提供了大量練習題和解答手冊,以幫助課堂教學。在可能的情況下,強調應用為中心的觀點,以便理解每個技術類別的實際用途。