Machine Learning With Go · Implement Regression, Classification, Clustering, Time-Series Models, Neural Networks, and More Using the Go Programming Language by Whitenack, Daniel -- Read -- Imperial Library of Trantor

Index

Preface

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

Gathering and Organizing Data

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

Matrices, Probability, and Statistics

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 plots

Probability

Random variables Probability measures Independent and conditional probability Hypothesis testing

Test statistics Calculating p-values

References Summary

Evaluation and Validation

Evaluation

Continuous metrics Categorical metrics

Individual evaluation metrics for categorical variables Confusion matrices, AUC, and ROC

Validation

Training and test sets Holdout set Cross validation

References Summary

Regression

Understanding regression model jargon Linear regression

Overview of linear regression Linear regression assumptions and pitfalls Linear regression example

Profiling the data Choosing our independent variable Creating our training and test sets Training our model Evaluating the trained model

Multiple linear regression Nonlinear and other types of regression References Summary

Classification

Understanding classification model jargon Logistic regression

Overview of logistic regression Logistic regression assumptions and pitfalls Logistic regression example

Cleaning and profiling the data Creating our training and test sets Training and testing the logistic regression model

k-nearest neighbors

Overview of kNN kNN assumptions and pitfalls kNN example

Decision trees and random forests

Overview of decision trees and random forests Decision tree and random forest assumptions and pitfalls Decision tree example Random forest example

Naive bayes

Overview of naive bayes and its big assumption Naive bayes example

References Summary

Clustering

Understanding clustering model jargon Measuring Distance or Similarity Evaluating clustering techniques

Internal clustering evaluation External clustering evaluation

k-means clustering

Overview of k-means clustering k-means assumptions and pitfalls k-means clustering example

Profiling the data Generating clusters with k-means Evaluating the generated clusters

Other clustering techniques References Summary

Time Series and Anomaly Detection

Representing time series data in Go Understanding time series jargon Statistics related to time series

Autocorrelation Partial autocorrelation

Auto-regressive models for forecasting

Auto-regressive model overview Auto-regressive model assumptions and pitfalls Auto-regressive model example

Transforming to a stationary series Analyzing the ACF and choosing an AR order Fitting and evaluating an AR(2) model

Auto-regressive moving averages and other time series models Anomaly detection References Summary

Neural Networks and Deep Learning

Understanding neural net jargon Building a simple neural network

Nodes in the network Network architecture Why do we expect this architecture to work? Training our neural network

Utilizing the simple neural network

Training the neural network on real data Evaluating the neural network

Introducing deep learning

What is a deep learning model? Deep learning with Go

Setting up TensorFlow for use with Go Retrieving and calling a pretrained TensorFlow model Object detection using TensorFlow from Go

References Summary

Deploying and Distributing Analyses and Models

Running models reliably on remote machines

A brief introduction to Docker and Docker jargon Docker-izing a machine learning application

Docker-izing the model training and export Docker-izing model predictions Testing the Docker images locally Running the Docker images on remote machines

Building a scalable and reproducible machine learning pipeline

Setting up a Pachyderm and Kubernetes cluster Building a Pachyderm machine learning pipeline

Creating and filling the input repositories Creating and running the processing stages

Updating pipelines and examining provenance Scaling pipeline stages

References Summary

Algorithms/Techniques Related to Machine Learning

Gradient descent Entropy, information gain, and related methods Backpropagation

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