Hands-on unsupervised learning with Python :: implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more /
Unsupervised learning is a key required block in both machine learning and deep learning domains. You will explore how to make your models learn, grow, change, and develop by themselves whenever they are exposed to a new set of data. With this book, you will learn the art of unsupervised learning fo...
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing Ltd,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Unsupervised learning is a key required block in both machine learning and deep learning domains. You will explore how to make your models learn, grow, change, and develop by themselves whenever they are exposed to a new set of data. With this book, you will learn the art of unsupervised learning for different real-world challenges. |
| Beschreibung: | PCA with the MNIST dataset |
| Beschreibung: | 1 online resource (375 pages) |
| ISBN: | 1789349273 9781789349276 |
Internformat
MARC
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| 245 | 1 | 0 | |a Hands-on unsupervised learning with Python : |b implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more / |c Giuseppe Bonaccorso. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2019. | ||
| 300 | |a 1 online resource (375 pages) | ||
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Unsupervised Learning; Technical requirements; Why do we need machine learning?; Descriptive analysis; Diagnostic analysis; Predictive analysis; Prescriptive analysis; Types of machine learning algorithm; Supervised learning algorithms; Supervised hello world!; Unsupervised learning algorithms; Cluster analysis; Generative models; Association rules; Unsupervised hello world!; Semi-supervised learning algorithms; Reinforcement learning algorithms | |
| 505 | 8 | |a Why Python for data science and machine learning?Summary; Questions; Further reading; Chapter 2: Clustering Fundamentals; Technical requirements; Introduction to clustering; Distance functions; K-means; K-means++; Analysis of the Breast Cancer Wisconsin dataset; Evaluation metrics; Minimizing the inertia; Silhouette score; Completeness score; Homogeneity score; A trade-off between homogeneity and completeness using the V-measure; Adjusted Mutual Information (AMI) score; Adjusted Rand score; Contingency matrix; K-Nearest Neighbors; Vector Quantization; Summary; Questions; Further reading | |
| 505 | 8 | |a Chapter 3: Advanced ClusteringTechnical requirements; Spectral clustering; Mean shift; DBSCAN; Calinski-Harabasz score; Analysis of the Absenteeism at Work dataset using DBSCAN; Cluster instability as a performance metric; K-medoids; Online clustering; Mini-batch K-means; BIRCH; Comparison between mini-batch K-means and BIRCH; Summary; Questions; Further reading; Chapter 4: Hierarchical Clustering in Action; Technical requirements; Cluster hierarchies; Agglomerative clustering; Single and complete linkages; Average linkage; Ward's linkage; Analyzing a dendrogram | |
| 505 | 8 | |a Cophenetic correlation as a performance metricAgglomerative clustering on the Water Treatment Plant dataset; Connectivity constraints; Summary; Questions; Further reading; Chapter 5: Soft Clustering and Gaussian Mixture Models; Technical requirements; Soft clustering; Fuzzy c-means; Gaussian mixture; EM algorithm for Gaussian mixtures; Assessing the performance of a Gaussian mixture with AIC and BIC; Component selection using Bayesian Gaussian mixture; Generative Gaussian mixture; Summary; Questions; Further reading; Chapter 6: Anomaly Detection; Technical requirements | |
| 505 | 8 | |a Probability density functionsAnomalies as outliers or novelties; Structure of the dataset; Histograms; Kernel density estimation (KDE); Gaussian kernel; Epanechnikov kernel; Exponential kernel; Uniform (or Tophat) kernel; Estimating the density; Anomaly detection; Anomaly detection with the KDD Cup 99 dataset; One-class support vector machines; Anomaly detection with Isolation Forests; Summary; Questions; Further reading; Chapter 7: Dimensionality Reduction and Component Analysis; Technical requirements; Principal Component Analysis (PCA); PCA with Singular Value Decomposition; Whitening | |
| 500 | |a PCA with the MNIST dataset | ||
| 520 | |a Unsupervised learning is a key required block in both machine learning and deep learning domains. You will explore how to make your models learn, grow, change, and develop by themselves whenever they are exposed to a new set of data. With this book, you will learn the art of unsupervised learning for different real-world challenges. | ||
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| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 6 | |a Python (Langage de programmation) | |
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| 776 | 0 | 8 | |i Print version: |a Bonaccorso, Giuseppe. |t Hands-On Unsupervised Learning with Python : Implement Machine Learning and Deep Learning Models Using Scikit-Learn, TensorFlow, and More. |d Birmingham : Packt Publishing Ltd, ©2019 |z 9781789348279 |
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| contents | Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Unsupervised Learning; Technical requirements; Why do we need machine learning?; Descriptive analysis; Diagnostic analysis; Predictive analysis; Prescriptive analysis; Types of machine learning algorithm; Supervised learning algorithms; Supervised hello world!; Unsupervised learning algorithms; Cluster analysis; Generative models; Association rules; Unsupervised hello world!; Semi-supervised learning algorithms; Reinforcement learning algorithms Why Python for data science and machine learning?Summary; Questions; Further reading; Chapter 2: Clustering Fundamentals; Technical requirements; Introduction to clustering; Distance functions; K-means; K-means++; Analysis of the Breast Cancer Wisconsin dataset; Evaluation metrics; Minimizing the inertia; Silhouette score; Completeness score; Homogeneity score; A trade-off between homogeneity and completeness using the V-measure; Adjusted Mutual Information (AMI) score; Adjusted Rand score; Contingency matrix; K-Nearest Neighbors; Vector Quantization; Summary; Questions; Further reading Chapter 3: Advanced ClusteringTechnical requirements; Spectral clustering; Mean shift; DBSCAN; Calinski-Harabasz score; Analysis of the Absenteeism at Work dataset using DBSCAN; Cluster instability as a performance metric; K-medoids; Online clustering; Mini-batch K-means; BIRCH; Comparison between mini-batch K-means and BIRCH; Summary; Questions; Further reading; Chapter 4: Hierarchical Clustering in Action; Technical requirements; Cluster hierarchies; Agglomerative clustering; Single and complete linkages; Average linkage; Ward's linkage; Analyzing a dendrogram Cophenetic correlation as a performance metricAgglomerative clustering on the Water Treatment Plant dataset; Connectivity constraints; Summary; Questions; Further reading; Chapter 5: Soft Clustering and Gaussian Mixture Models; Technical requirements; Soft clustering; Fuzzy c-means; Gaussian mixture; EM algorithm for Gaussian mixtures; Assessing the performance of a Gaussian mixture with AIC and BIC; Component selection using Bayesian Gaussian mixture; Generative Gaussian mixture; Summary; Questions; Further reading; Chapter 6: Anomaly Detection; Technical requirements Probability density functionsAnomalies as outliers or novelties; Structure of the dataset; Histograms; Kernel density estimation (KDE); Gaussian kernel; Epanechnikov kernel; Exponential kernel; Uniform (or Tophat) kernel; Estimating the density; Anomaly detection; Anomaly detection with the KDD Cup 99 dataset; One-class support vector machines; Anomaly detection with Isolation Forests; Summary; Questions; Further reading; Chapter 7: Dimensionality Reduction and Component Analysis; Technical requirements; Principal Component Analysis (PCA); PCA with Singular Value Decomposition; Whitening |
| ctrlnum | (OCoLC)1089524139 |
| dewey-full | 005.13/3 |
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| spelling | Bonaccorso, Giuseppe, author. http://id.loc.gov/authorities/names/nr98027897 Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more / Giuseppe Bonaccorso. Birmingham : Packt Publishing Ltd, 2019. 1 online resource (375 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Unsupervised Learning; Technical requirements; Why do we need machine learning?; Descriptive analysis; Diagnostic analysis; Predictive analysis; Prescriptive analysis; Types of machine learning algorithm; Supervised learning algorithms; Supervised hello world!; Unsupervised learning algorithms; Cluster analysis; Generative models; Association rules; Unsupervised hello world!; Semi-supervised learning algorithms; Reinforcement learning algorithms Why Python for data science and machine learning?Summary; Questions; Further reading; Chapter 2: Clustering Fundamentals; Technical requirements; Introduction to clustering; Distance functions; K-means; K-means++; Analysis of the Breast Cancer Wisconsin dataset; Evaluation metrics; Minimizing the inertia; Silhouette score; Completeness score; Homogeneity score; A trade-off between homogeneity and completeness using the V-measure; Adjusted Mutual Information (AMI) score; Adjusted Rand score; Contingency matrix; K-Nearest Neighbors; Vector Quantization; Summary; Questions; Further reading Chapter 3: Advanced ClusteringTechnical requirements; Spectral clustering; Mean shift; DBSCAN; Calinski-Harabasz score; Analysis of the Absenteeism at Work dataset using DBSCAN; Cluster instability as a performance metric; K-medoids; Online clustering; Mini-batch K-means; BIRCH; Comparison between mini-batch K-means and BIRCH; Summary; Questions; Further reading; Chapter 4: Hierarchical Clustering in Action; Technical requirements; Cluster hierarchies; Agglomerative clustering; Single and complete linkages; Average linkage; Ward's linkage; Analyzing a dendrogram Cophenetic correlation as a performance metricAgglomerative clustering on the Water Treatment Plant dataset; Connectivity constraints; Summary; Questions; Further reading; Chapter 5: Soft Clustering and Gaussian Mixture Models; Technical requirements; Soft clustering; Fuzzy c-means; Gaussian mixture; EM algorithm for Gaussian mixtures; Assessing the performance of a Gaussian mixture with AIC and BIC; Component selection using Bayesian Gaussian mixture; Generative Gaussian mixture; Summary; Questions; Further reading; Chapter 6: Anomaly Detection; Technical requirements Probability density functionsAnomalies as outliers or novelties; Structure of the dataset; Histograms; Kernel density estimation (KDE); Gaussian kernel; Epanechnikov kernel; Exponential kernel; Uniform (or Tophat) kernel; Estimating the density; Anomaly detection; Anomaly detection with the KDD Cup 99 dataset; One-class support vector machines; Anomaly detection with Isolation Forests; Summary; Questions; Further reading; Chapter 7: Dimensionality Reduction and Component Analysis; Technical requirements; Principal Component Analysis (PCA); PCA with Singular Value Decomposition; Whitening PCA with the MNIST dataset Unsupervised learning is a key required block in both machine learning and deep learning domains. You will explore how to make your models learn, grow, change, and develop by themselves whenever they are exposed to a new set of data. With this book, you will learn the art of unsupervised learning for different real-world challenges. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Langage de programmation) Intelligence artificielle. Apprentissage automatique. artificial intelligence. aat COMPUTERS Programming Languages Python. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast Print version: Bonaccorso, Giuseppe. Hands-On Unsupervised Learning with Python : Implement Machine Learning and Deep Learning Models Using Scikit-Learn, TensorFlow, and More. Birmingham : Packt Publishing Ltd, ©2019 9781789348279 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2037536 Volltext |
| spellingShingle | Bonaccorso, Giuseppe Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more / Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Unsupervised Learning; Technical requirements; Why do we need machine learning?; Descriptive analysis; Diagnostic analysis; Predictive analysis; Prescriptive analysis; Types of machine learning algorithm; Supervised learning algorithms; Supervised hello world!; Unsupervised learning algorithms; Cluster analysis; Generative models; Association rules; Unsupervised hello world!; Semi-supervised learning algorithms; Reinforcement learning algorithms Why Python for data science and machine learning?Summary; Questions; Further reading; Chapter 2: Clustering Fundamentals; Technical requirements; Introduction to clustering; Distance functions; K-means; K-means++; Analysis of the Breast Cancer Wisconsin dataset; Evaluation metrics; Minimizing the inertia; Silhouette score; Completeness score; Homogeneity score; A trade-off between homogeneity and completeness using the V-measure; Adjusted Mutual Information (AMI) score; Adjusted Rand score; Contingency matrix; K-Nearest Neighbors; Vector Quantization; Summary; Questions; Further reading Chapter 3: Advanced ClusteringTechnical requirements; Spectral clustering; Mean shift; DBSCAN; Calinski-Harabasz score; Analysis of the Absenteeism at Work dataset using DBSCAN; Cluster instability as a performance metric; K-medoids; Online clustering; Mini-batch K-means; BIRCH; Comparison between mini-batch K-means and BIRCH; Summary; Questions; Further reading; Chapter 4: Hierarchical Clustering in Action; Technical requirements; Cluster hierarchies; Agglomerative clustering; Single and complete linkages; Average linkage; Ward's linkage; Analyzing a dendrogram Cophenetic correlation as a performance metricAgglomerative clustering on the Water Treatment Plant dataset; Connectivity constraints; Summary; Questions; Further reading; Chapter 5: Soft Clustering and Gaussian Mixture Models; Technical requirements; Soft clustering; Fuzzy c-means; Gaussian mixture; EM algorithm for Gaussian mixtures; Assessing the performance of a Gaussian mixture with AIC and BIC; Component selection using Bayesian Gaussian mixture; Generative Gaussian mixture; Summary; Questions; Further reading; Chapter 6: Anomaly Detection; Technical requirements Probability density functionsAnomalies as outliers or novelties; Structure of the dataset; Histograms; Kernel density estimation (KDE); Gaussian kernel; Epanechnikov kernel; Exponential kernel; Uniform (or Tophat) kernel; Estimating the density; Anomaly detection; Anomaly detection with the KDD Cup 99 dataset; One-class support vector machines; Anomaly detection with Isolation Forests; Summary; Questions; Further reading; Chapter 7: Dimensionality Reduction and Component Analysis; Technical requirements; Principal Component Analysis (PCA); PCA with Singular Value Decomposition; Whitening Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Langage de programmation) Intelligence artificielle. Apprentissage automatique. artificial intelligence. aat COMPUTERS Programming Languages Python. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh85079324 |
| title | Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more / |
| title_auth | Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more / |
| title_exact_search | Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more / |
| title_full | Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more / Giuseppe Bonaccorso. |
| title_fullStr | Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more / Giuseppe Bonaccorso. |
| title_full_unstemmed | Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more / Giuseppe Bonaccorso. |
| title_short | Hands-on unsupervised learning with Python : |
| title_sort | hands on unsupervised learning with python implement machine learning and deep learning models using scikit learn tensorflow and more |
| title_sub | implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Langage de programmation) Intelligence artificielle. Apprentissage automatique. artificial intelligence. aat COMPUTERS Programming Languages Python. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Artificial intelligence. Machine learning. Python (Langage de programmation) Intelligence artificielle. Apprentissage automatique. artificial intelligence. COMPUTERS Programming Languages Python. Artificial intelligence Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2037536 |
| work_keys_str_mv | AT bonaccorsogiuseppe handsonunsupervisedlearningwithpythonimplementmachinelearninganddeeplearningmodelsusingscikitlearntensorflowandmore |