Machine Learning With Go, Second Edition by Whitenack, Daniel -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Machine Learning With Go Second Edition

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Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Section 1: Analysis in Machine Learning Workflows 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 data types Manipulating CSV data with data frames

Web scraping 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 or installing Pachyderm Creating data repositories for data versioning Putting data into data repositories Getting data out of versioned data repositories

Summary References

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

Bivariate analysis

Probability

Random variables Probability measures Independent and conditional probability Hypothesis testing

Test statistics Calculating p-values

Summary References

Evaluating and Validating

Evaluating

Continuous metrics Categorical metrics

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

Validating

Training and test sets Holdout set Cross-validation

Summary References

Section 2: Machine Learning Techniques 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 Summary References

Classification

Understanding classification model jargon Logistic regression

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

Cleaning and profiling 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

Summary References

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 Summary References

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 into 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 Summary References

Section 3: Advanced Machine Learning, Deployment, and Scaling Neural Networks

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

Summary References

Deep Learning

Deep learning techniques and jargon Deep learning with Go

Using the TensorFlow Go bindings

Install TensorFlow for Go Retrieving and calling a pretrained TensorFlow model Object detection using TensorFlow from Go

Using TensorFlow models from GoCV

Installing GoCV Streaming webcam object detection with GoCV

Summary References

Deploying and Distributing Analyses and Models

Running models reliably on remote machines

A brief introduction to Docker and Docker jargon Dockerizing a machine learning application

Dockerizing the model training and export Dockerizing 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 a 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

Summary References

Algorithms/Techniques Related to Machine Learning

Gradient descent Entropy, information gain, and related methods Backpropagation

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