Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

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Table of ContentsChapter 5: Deep Learning and Neural Networks99viiiBackpropagation100Backpropagation Approach100Generalized Delta Rule100Update of Output Layer Weights101Update of Hidden Layer Weights102BPN Summary103Backpropagation Algorithm104Other Algorithms106TensorFlow106Recurrent Neural Network113Chapter 6: Time Series121Classification of Variation121Analyzing a Series Containing a Trend121Curve Fitting122Removing Trends from a Time Series123Analyzing a Series Containing Seasonality124Removing Seasonality from a Time Series125By Filtering125By Differencing126Transformation126To Stabilize the Variance126To Make the Seasonal Effect Additive127To Make the Data Distribution Normal127Stationary Time Series128Stationary Process128Autocorrelation and the Correlogram129Estimating Autocovariance and Autocorrelation Functions129
Table of ContentsTime-Series Analysis with Python130Useful Methods131Autoregressive Processes133Estimating Parameters of an AR Process134Mixed ARMA Models137Integrated ARMA Models138The Fourier Transform140An Exceptional Scenario141Missing Data143Chapter 7: Analytics at Scale145Hadoop145MapReduce Programming145Partitioning Function146Combiner Function147HDFS File System159MapReduce Design Pattern159Spark166Analytics in the Cloud168Internet of Things179Index181ix
Page 1 and 2: AdvancedData AnalyticsUsing PythonW
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Page 5 and 6: Table of ContentsAbout the Authorxi
Page 7: Table of ContentsNaive Bayes Classi
Page 11 and 12: About the Technical ReviewerSundar
Page 13 and 14: CHAPTER 1IntroductionIn this book,
Page 15 and 16: Chapter 1IntroductionOOP in PythonB
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Page 19 and 20: Chapter 1Introductiondef parseSoupT
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Page 27 and 28: Chapter 1Introductiong.scores = Tab
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Page 35 and 36: CHAPTER 2ETL with Python(Structured
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Chapter 2ETL with Python (Structure
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CHAPTER 3Supervised LearningUsing P
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Chapter 3Supervised Learning Using
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Decision TreeChapter 3 Supervised L
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CHAPTER 4UnsupervisedLearning: Clus
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Chapter 4Unsupervised Learning: Clu
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General and Euclidean DistanceThe d
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Chapter 4Graph Theoretical Approach
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CHAPTER 5Deep Learningand Neural Ne
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Chapter 5Deep Learning and Neural N
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Chapter 6Time SeriesFigure 6-1. A t
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Chapter 6Time SeriesA trend can be
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Chapter 6Time SeriesThe simple movi
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Chapter 6Time SeriesIrregular Fluct
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Chapter 6Time SeriesFigure 6-2 show
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Chapter 6Time Seriesìïïr ( k)=í
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Chapter 6Time SeriesThe autocovaria
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Chapter 6Time SeriesIn this case, r
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Chapter 6Time SeriesHere is how to
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Chapter 6 Time SeriesThe Fourier Tr
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Chapter 6Time Seriesmodel provides
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CHAPTER 7Analytics at ScaleIn recen
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Chapter 7Analytics at Scalealphabet
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Chapter 7Analytics at Scalepublic a
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Chapter 7Analytics at ScaleHere is
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Chapter 7Analytics at Scaleimport o
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Chapter 7Analytics at ScaleRootBDAS
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Chapter 7Analytics at ScaleTo test
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Chapter 7Analytics at ScaleHDFS Fil
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Chapter 7Analytics at ScaleJoin Pat
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Chapter 7Analytics at Scale}{}Strin
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Chapter 7Analytics at Scale}}String
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Chapter 7Analytics at ScaleSpark Co
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Chapter 7Analytics at Scalespeech p
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Chapter 7Analytics at Scaley=height
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Chapter 7Analytics at Scaleif not s
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Chapter 7Analytics at Scaleelse:(pr
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Chapter 7Analytics at Scalefor futu
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Chapter 7Analytics at ScaleYou can
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IndexCollaborative filtering, 52Com
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IndexNeo4j, 34Neo4j REST, 35Neural
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IndexTime series (cont.)transformat
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Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

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