Go语言实现机器学习：回归、分类到神经网络

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《Go语言机器学习》是一本由Daniel Whitenack编写的教程，专注于利用Go编程语言实现各种机器学习算法。该书内容涵盖了机器学习的核心领域，包括但不限于： 1. **回归**（Regression）：Go语言中的机器学习应用会涉及到预测连续变量的方法，如线性回归、多项式回归等，通过训练数据来拟合输入与输出之间的关系。 2. **分类**（Classification）：书中会讲解如何在Go中构建分类模型，例如逻辑回归、决策树、随机森林和支持向量机等，用于将数据分为多个预定义类别。 3. **聚类**（Clustering）：介绍如何使用Go实现K-means、层次聚类等算法，这些方法用于无监督学习，自动发现数据集中的自然群体或结构。 4. **时间序列分析**（Time-series Models）：书中可能会涉及ARIMA模型、指数平滑等技术，帮助读者处理具有时间依赖性的数据，如股票价格、天气预报等。 5. **神经网络**（Neural Networks）：Go虽然可能不是传统上用于深度学习的首选语言，但作者会展示如何在Go中构建简单的神经网络，如前馈神经网络，甚至浅层网络，用于图像识别或文本处理等任务。 6. **更多内容**：除了上述主要部分，这本书还可能涵盖其他机器学习技术，如特征选择、数据预处理、模型评估和优化等实用技巧。《Go语言机器学习》的版权信息表明，这本书是2017年Packt Publishing出版的，所有内容未经许可不得复制、存储或传播。尽管作者和出版社已尽力确保信息的准确性，但书中提供的所有资料均不带任何形式的保证，用户使用时需自行承担风险。Packt Publishing在提供商标信息方面也表现出了应有的尊重和合规性。《Go语言机器学习》是一本适合对Go语言感兴趣且希望掌握机器学习基础的开发者深入学习的实用指南。

Preface

[ 2 ]

Chapter 2, Matrices, Probability, and Statistics, covers the organization of data into

matrices and matrix operations. Once the reader is done with this material, they

will understand how to form matrices within Go programs and how to utilize

these matrices to perform various types of matrix operations. This chapter also

covers statistical measures and operations key to day-to-day data analysis work.

Once the reader is done with this chapter, they will understand how to perform

solid summary data analysis, describe and visualize distributions, quantify

hypotheses, and transform datasets with, for example, dimensionality reductions.

Chapter 3, Evaluation and Validation, covers evaluation and validation, which are

key to measuring the performance of machine applications and ensuring that

they generalize. Once the reader is done with this chapter, they will understand

various metrics to gauge the performance of models (in other words, evaluate the

model) as well as various techniques to validate the model more generally.

Machine learning techniques:

Chapter 4, Regression, explains regression, a widely used technique to model

continuous variables and a basis for other models. Regression produces models

that are immediately interpretable. Thus, it can provide an excellent starting

point when introducing predictive capabilities in a organization.

Chapter 5, Classification, covers classification, a machine learning technique

distinct from regression in that the target variable is typically categorical or

labeled. For example, a classification model may classify emails into spam and

not spam categories or classify network traffic as fraudulent or not fraudulent.

Chapter 6, Clustering, covers clustering, an unsupervised machine learning

technique used to form groupings of samples. At the end of this chapter, readers

will be able to automatically form groupings of data points to better understand

their structure.

Chapter 7, Time Series and Anomaly Detection, introduces techniques utilized to

model time series data, such as stock prices, user events, and so on. After reading

this chapter, the reader will understand how to evaluate various terms in a time

series, build up a model of the time series, and detect anomalies in a time series.

Taking machine learning to the next level:

Chapter 8, Neural Networks and Deep Learning, introduces techniques utilized to

perform regression, classification, and image processing with neural networks.

After reading this chapter, the reader will understand how and when to apply

these more complicated modeling techniques.

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