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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Bonaccorso, Giuseppe, |e author. | |
| 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 Python (Computer program language) | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Apprentissage automatique. | |
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| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 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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