Machine Learning With Go电子书

Title Page

Copyright

Machine Learning With Go

Credits

About the Author

About the Reviewers

www.PacktPub.com

Why subscribe?

Customer Feedback

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