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莫易惜
2019-05-30 11:13:21
自学能力,阅读能力,情绪管理,英语阅读能力
分区,知识分类 刻意练习,走出舒适区 反馈,审查和提高
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自学能力,阅读能力,情绪管理,英语阅读能力 分区,知识分类 刻意练习,走出舒适区 反馈,审查和提高
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Fundamentals.of.Machine.L
ear
ning
.for.Predictive.Data.Analytics.02620294
Machine l
ear
ning
is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine l
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ning
approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine l
ear
ning
: information-based l
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ning
, similarity-based l
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ning
, probability-based l
ear
ning
, and error-based l
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ning
. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many y
ear
s of teaching machine l
ear
ning
, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals. Table of Contents Chapter 1 Machine L
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for Predictive Data Analytics Chapter 2 Data to Insights to Decisions Chapter 3 Data Exploration Chapter 4 Information-based L
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Chapter 5 Similarity-based L
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Chapter 6 Probability-based L
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Chapter 7 Error-based L
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Chapter 8 Evaluation Chapter 9 Case Study: Customer Churn Chapter 10 Case Study: Galaxy Classification Chapter 11 The Art of Machine L
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for Predictive Data Analytics Appendix A Descriptive Statistics and Data Visualization for Machine L
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Appendix B Introduction to Probability for Machine L
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Appendix C Differentiation Techniques for Machine L
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Machine.L
ear
ning
.An.Algorithmic.Perspective.2nd.Edition.1466583282
Title: Machine L
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: An Algorithmic Perspective, 2nd Edition Author: Stephen Marsland Length: 457 pages Edition: 2 Language: English Publisher: Chapman and Hall/CRC Publication Date: 2014-10-08 ISBN-10: 1466583282 ISBN-13: 9781466583283 A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine l
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ning
, including the increasing work on the statistical interpretations of machine l
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ning
algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine L
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ning
: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine l
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ning
. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. New to the Second Edition Two new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle filters Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website. Table of Contents Chapter 1: Introduction Chapter 2: Preliminaries Chapter 3: Neurons, Neural Networks,and Lin
ear
Discriminants Chapter 4: The Multi-layer Perceptron Chapter 5: Radial Basis Functions andSplines Chapter 6: Dimensionality Reduction Chapter 7: Probabilistic L
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Chapter 8: Support Vector Machines Chapter 9: Optimisation and S
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ch Chapter 10: Evolutionary L
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Chapter 11: Reinforcement L
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Chapter 12: L
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with Trees Chapter 13: Decision by Committee:Ensemble L
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Chapter 14: Unsupervised L
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Chapter 15: Markov Chain Monte Carlo(MCMC) Methods Chapter 16: Graphical Models Chapter 17: Symmetric Weights and DeepBelief Networks Chapter 18: Gaussian Processes
机器学习-Q-L
ear
ning
-沙鼠走迷宫视频教学
通过有趣的沙鼠走迷宫游戏,让大家掌握Q-学习算法的实质理论,并且帮助学院去动手写一个让机器思考的程序,理解机器学习。
Bengio写的MIT Press《Deep l
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ning
》PDF整理版
深度学习,MIT,deep l
ear
ning
, Yoshua Bengio,Ian Goodfellow,Aaron Courville
L
ear
ning
.Spark.Light
ning
-Fast.Big.Data.Analysis.pdf
L
ear
ning
Spark, pdf格式, 为数不多的spark著作,值得细看
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