舉報 
會員
Machine Learning with Go Quick Start Guide
Machinelearningisanessentialpartoftoday'sdata-drivenworldandisextensivelyusedacrossindustries,includingfinancialforecasting,robotics,andwebtechnology.ThisbookwillteachyouhowtoefficientlydevelopmachinelearningapplicationsinGo.Thebookstartswithanintroductiontomachinelearninganditsdevelopmentprocess,explainingthetypesofproblemsthatitaimstosolveandthesolutionsitoffers.ItthencoverssettingupafrictionlessGodevelopmentenvironment,includingrunningGointeractivelywithJupyternotebooks.Finally,commondataprocessingtechniquesareintroduced.Thebookthenteachesthereaderaboutsupervisedandunsupervisedlearningtechniquesthroughworkedexamplesthatincludetheimplementationofevaluationmetrics.Theseworkedexamplesmakeuseoftheprominentopen-sourcelibrariesGoMLandGonum.Thebookalsoteachesreadershowtoloadapre-trainedmodelanduseittomakepredictions.Itthenmovesontotheoperationalsideofrunningmachinelearningapplications:deployment,ContinuousIntegration,andhelpfuladviceforeffectiveloggingandmonitoring.Attheendofthebook,readerswilllearnhowtosetupamachinelearningprojectforsuccess,formulatingrealisticsuccesscriteriaandaccuratelytranslatingbusinessrequirementsintotechnicalones.
目錄(140章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Machine Learning with Go Quick Start Guide
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the authors
- About the reviewers
- Packt is searching for authors like you
- 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
- Get in touch
- Reviews
- Introducing Machine Learning with Go
- What is ML?
- Types of ML algorithms
- Supervised learning problems
- Unsupervised learning problems
- Why write ML applications in Go?
- The advantages of Go
- Go's mature ecosystem
- Transfer knowledge and models created in other languages
- ML development life cycle
- Defining problem and objectives
- Acquiring and exploring data
- Selecting the algorithm
- Preparing data
- Training
- Validating/testing
- Integrating and deploying
- Re-validating
- Summary
- Further readings
- Setting Up the Development Environment
- Installing Go
- Linux macOS and FreeBSD
- Windows
- Running Go interactively with gophernotes
- Example – the most common phrases in positive and negative reviews
- Initializing the example directory and downloading the dataset
- Loading the dataset files
- Parsing contents into a Struct
- Loading the data into a Gota dataframe
- Finding the most common phrases
- Example – exploring body mass index data with gonum/plot
- Installing gonum and gonum/plot
- Loading the data
- Understanding the distributions of the data series
- Example – preprocessing data with Gota
- Loading the data into Gota
- Removing and renaming columns
- Converting a column into a different type
- Filtering out unwanted data
- Normalizing the Height Weight and Age columns
- Sampling to obtain training/validation subsets
- Encoding data with categorical variables
- Summary
- Further readings
- Supervised Learning
- Classification
- A simple model – the logistic classifier
- Measuring performance
- Precision and recall
- ROC curves
- Multi-class models
- A non-linear model – the support vector machine
- Overfitting and underfitting
- Deep learning
- Neural networks
- A simple deep learning model architecture
- Neural network training
- Regression
- Linear regression
- Random forest regression
- Other regression models
- Summary
- Further readings
- Unsupervised Learning
- Clustering
- Principal component analysis
- Summary
- Further readings
- Using Pretrained Models
- How to restore a saved GoML model
- Deciding when to adopt a polyglot approach
- Example – invoking a Python model using os/exec
- Example – invoking a Python model using HTTP
- Example – deep learning using the TensorFlow API for Go
- Installing TensorFlow
- Import the pretrained TensorFlow model
- Creating inputs to the TensorFlow model
- Summary
- Further readings
- Deploying Machine Learning Applications
- The continuous delivery feedback loop
- Developing
- Testing
- Deployment
- Dependencies
- Model persistence
- Monitoring
- Structured logging
- Capturing metrics
- Feedback
- Deployment models for ML applications
- Infrastructure-as-a-service
- Amazon Web Services
- Microsoft Azure
- Google Cloud
- Platform-as-a-Service
- Amazon Web Services
- Amazon Sagemaker
- Amazon AI Services
- Microsoft Azure
- Azure ML Studio
- Azure Cognitive Services
- Google Cloud
- AI Platform
- AI Building Blocks
- Summary
- Further readings
- Conclusion - Successful ML Projects
- When to use ML
- Typical stages in a ML project
- Business and data understanding
- Data preparation
- Modelling and evaluation
- Deployment
- When to combine ML with traditional code
- Summary
- Further readings
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-24 13:34:20
推薦閱讀
- Applied Unsupervised Learning with R
- 辦公通信設備維修
- 深入淺出SSD：固態存儲核心技術、原理與實戰
- Manage Partitions with GParted How-to
- 電腦維護365問
- VCD、DVD原理與維修
- Rapid BeagleBoard Prototyping with MATLAB and Simulink
- OpenGL Game Development By Example
- Arduino BLINK Blueprints
- Intel Edison智能硬件開發指南：基于Yocto Project
- 微型計算機系統原理及應用：國產龍芯處理器的軟件和硬件集成（基礎篇）
- Managing Data and Media in Microsoft Silverlight 4：A mashup of chapters from Packt's bestselling Silverlight books
- The Applied Artificial Intelligence Workshop
- Corona SDK Mobile Game Development：Beginner's Guide
- Service Mesh微服務架構設計
- Hands-On Embedded Programming with C++17
- Arduino項目開發：智能控制
- 精選單片機設計與制作30例
- Avid Media Composer 6.x Cookbook
- Mastering Adobe Photoshop Elements 2020
- Nginx應用與運維實戰
- 持久內存架構與工程實踐
- Blender 3D Printing Essentials
- 微服務之道：度量驅動開發
- 筆記本電腦維修從入門到精通
- Apache Kylin權威指南（第2版）
- 基于C語言與Proteus聯合仿真的單片機技術
- 嵌入式系統軟硬件協同設計實戰指南：基于Xilinx ZYNQ（第2版）
- Raspberry Pi Computer Architecture Essentials
- 新硬件主義
