機(jī)器學(xué)習(xí)算法(第2版影印版)(英文版)
定 價(jià)：108 元
- 作者：Giuseppe Bonaccorso著
- 出版時(shí)間：2019/3/1
- ISBN：9787564182915
- 出 版 社：東南大學(xué)出版社
- 中圖法分類：TP181
- 頁碼：508頁
- 紙張：膠版紙
- 版次：1
- 開本：16K
本書內(nèi)容包括：研究特征提取與特征工程過程、評(píng)估線性回歸的性能和誤差估計(jì)、使用不同類型的算法構(gòu)建數(shù)據(jù)模型并理解其工作原理、調(diào)整支持向量機(jī)(SVM)的參數(shù)、探討自然語言處理(NLP)和推薦系統(tǒng)的概念、從頭開始創(chuàng)建一個(gè)機(jī)器學(xué)習(xí)架構(gòu)。
Preface
Chapter 1: A Gentle Introduction to Machine Learning
Introduction - classic and adaptive machines
Descriptive analysis
Predictive analysis
Only learning matters
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
Computational neuroscience
Beyond machine learning - deep learning and bio-inspired adaptive
systems
Machine learning and big data
Summary
Chapter 2: Important Elements in Machine Learning
Data formats
Multiclass strategies
One-vs-all
One-vs-one
Learnability
Underfitting and overfitting
Error measures and cost functions
PAC learning
Introduction to statistical learning concepts
MAP learning
Maximum likelihood learning
Class balancing
Resampling with replacement
SMOTE resampling
Elements of information theory
Entropy
Cross-entropy and mutual information
Divergence measures between two probability distributions
Summary
Chapter 3: Feature Selection and Feature Engineering
scikit-learn toy datasets
Creating training and test sets
Managing categorical data
Managing missing features
Data scaling and normalization
Whitening
Feature selection and filtering
Principal Component Analysis
Non-Negative Matrix Factorization
Sparse PCA
Kernel PCA
Independent Component Analysis
Atom extraction and dictionary learning
Visualizing high-dimensional datasets using t-SNE
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