Hello, World!

目录

Table of Contents

Preface

[Page 18-30, 12]

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions

Chapter 01. Gathering and Organizing Data

[Page 31-69, 38]

Handling data - Gopher style Best practices for gathering and organizing data with Go CSV files Reading in CSV data from a file Handling unexpected fields Handling unexpected types Manipulating CSV data with data frames JSON Parsing JSON JSON output SQL-like databases Connecting to an SQL database Querying the database Modifying the database Caching Caching data in memory Caching data locally on disk Data versioning Pachyderm jargon Deploying/installing Pachyderm Creating data repositories for data versioning Putting data into data repositories Getting data out of versioned data repositories References Summary

Chapter 2. Matrices, Probability, and Statistics

[Page 70-105, 35]

Matrices and vectors Vectors Vector operations Matrices Matrix operations Statistics Distributions Statistical measures Measures of central tendency Measures of spread or dispersion Visualizing distributions Histograms Box plotsProbabilityRandom variablesProbability measuresIndependent and conditional probabilityHypothesis testingTest statisticsCalculating p-valuesReferencesSummary

Chapter 3. Evaluation and Validation

[Page 105-131, 26]

EvaluationContinuous metricsCategorical metricsIndividual evaluation metrics for categorical variablesConfusion matrices, AUC, and ROCValidationTraining and test setsHoldout setCross validationReferencesSummary

Chapter 4. Regression

[Page 132-165, 33]

Understanding regression model jargonLinear regressionOverview of linear regressionLinear regression assumptions and pitfallsLinear regression example Profiling the dataChoosing our independent variableCreating our training and test setsTraining our modelEvaluating the trained modelMultiple linear regressionNonlinear and other types of regressionReferencesSummary

Chapter 5. Classification

[Page 166-205, 39]

Understanding classification model jargonLogistic regressionOverview of logistic regressionLogistic regression assumptions and pitfallsLogistic regression exampleCleaning and profiling the dataCreating our training and test setsTraining and testing the logistic regression modelk-nearest neighborsOverview of kNNkNN assumptions and pitfallskNN exampleDecision trees and random forestsOverview of decision trees and random forestsDecision tree and random forest assumptions and pitfallsDecision tree exampleRandom forest exampleNaive bayesOverview of naive bayes and its big assumptionNaive bayes exampleReferencesSummary

Chapter 6. Clustering

[Page 206-234, 28]

Understanding clustering model jargonMeasuring Distance or SimilarityEvaluating clustering techniquesInternal clustering evaluationExternal clustering evaluationk-means clusteringOverview of k-means clusteringk-means assumptions and pitfalls k-means clustering exampleProfiling the dataGenerating clusters with k-meansEvaluating the generated clustersOther clustering techniquesReferencesSummary

Chapter 7. Time Series and Anomaly Detection

[Page 235-267, 32]

Representing time series data in GoUnderstanding time series jargonStatistics related to time seriesAutocorrelationPartial autocorrelationAuto-regressive models for forecastingAuto-regressive model overviewAuto-regressive model assumptions and pitfallsAuto-regressive model exampleTransforming to a stationary seriesAnalyzing the ACF and choosing an AR orderFitting and evaluating an AR(2) modelAuto-regressive moving averages and other time series modelsAnomaly detectionReferencesSummary

Chapter 8. Neural Networks and Deep Learning

[Page 268-301, 33] Understanding neural net jargonBuilding a simple neural networkNodes in the networkNetwork architectureWhy do we expect this architecture to work?Training our neural networkUtilizing the simple neural networkTraining the neural network on real dataEvaluating the neural networkIntroducing deep learningWhat is a deep learning model?Deep learning with GoSetting up TensorFlow for use with GoRetrieving and calling a pretrained TensorFlow modelObject detection using TensorFlow from GoReferences Summary

Chapter 9. Deploying and Distributing Analyses and Models

[Page 302-341, 39]

Running models reliably on remote machinesA brief introduction to Docker and Docker jargonDocker-izing a machine learning applicationDocker-izing the model training and exportDocker-izing model predictionsTesting the Docker images locallyRunning the Docker images on remote machinesBuilding a scalable and reproducible machine learning pipelineSetting up a Pachyderm and Kubernetes clusterBuilding a Pachyderm machine learning pipelineCreating and filling the input repositoriesCreating and running the processing stagesUpdating pipelines and examining provenanceScaling pipeline stagesReferencesSummary

Chapter 10. Algorithms/Techniques Related to Machine Learning

[Page 342-351, 9] Gradient descentEntropy, information gain, and related methodsBackpropagation