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Mastering Machine Learning Algorithms : Expert techniques to implement popular machine learning algorithms and fine-tune your models.
This book is your guide to quickly get to grips with the most widely used machine learning algorithms. As a data science professional, this book will help you design and train better machine learning models to solve a variety of complex problems, and make the machine learn your requirements.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Bonaccorso c/o Quandoo, Giuseppe. | |
| 245 | 1 | 0 | |a Mastering Machine Learning Algorithms : |b Expert techniques to implement popular machine learning algorithms and fine-tune your models. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2018. | ||
| 300 | |a 1 online resource (567 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Machine Learning Model Fundamentals; Models and data; Zero-centering and whitening; Training and validation sets; Cross-validation; Features of a machine learning model; Capacity of a model; Vapnik-Chervonenkis capacity; Bias of an estimator; Underfitting; Variance of an estimator; Overfitting; The Cramér-Rao bound; Loss and cost functions; Examples of cost functions; Mean squared error; Huber cost function; Hinge cost function; Categorical cross-entropy; Regularization; Ridge; Lasso. | |
| 505 | 8 | |a ElasticNetEarly stopping; Summary; Chapter 2: Introduction to Semi-Supervised Learning; Semi-supervised scenario; Transductive learning; Inductive learning; Semi-supervised assumptions; Smoothness assumption; Cluster assumption; Manifold assumption; Generative Gaussian mixtures; Example of a generative Gaussian mixture; Weighted log-likelihood; Contrastive pessimistic likelihood estimation; Example of contrastive pessimistic likelihood estimation; Semi-supervised Support Vector Machines (S3VM); Example of S3VM; Transductive Support Vector Machines (TSVM); Example of TSVM; Summary. | |
| 505 | 8 | |a Chapter 3: Graph-Based Semi-Supervised LearningLabel propagation; Example of label propagation; Label propagation in Scikit-Learn; Label spreading; Example of label spreading; Label propagation based on Markov random walks; Example of label propagation based on Markov random walks; Manifold learning; Isomap; Example of Isomap; Locally linear embedding; Example of locally linear embedding; Laplacian Spectral Embedding; Example of Laplacian Spectral Embedding; t-SNE; Example of t-distributed stochastic neighbor embedding ; Summary; Chapter 4: Bayesian Networks and Hidden Markov Models. | |
| 505 | 8 | |a Conditional probabilities and Bayes' theoremBayesian networks; Sampling from a Bayesian network; Direct sampling; Example of direct sampling; A gentle introduction to Markov chains; Gibbs sampling; Metropolis-Hastings sampling; Example of Metropolis-Hastings sampling; Sampling example using PyMC3; Hidden Markov Models (HMMs); Forward-backward algorithm; Forward phase; Backward phase; HMM parameter estimation; Example of HMM training with hmmlearn; Viterbi algorithm; Finding the most likely hidden state sequence with hmmlearn; Summary; Chapter 5: EM Algorithm and Applications. | |
| 505 | 8 | |a MLE and MAP learningEM algorithm; An example of parameter estimation; Gaussian mixture; An example of Gaussian Mixtures using Scikit-Learn; Factor analysis; An example of factor analysis with Scikit-Learn; Principal Component Analysis; An example of PCA with Scikit-Learn; Independent component analysis; An example of FastICA with Scikit-Learn; Addendum to HMMs; Summary; Chapter 6: Hebbian Learning and Self-Organizing Maps; Hebb's rule; Analysis of the covariance rule; Example of covariance rule application; Weight vector stabilization and Oja's rule; Sanger's network. | |
| 500 | |a Example of Sanger's network. | ||
| 520 | |a This book is your guide to quickly get to grips with the most widely used machine learning algorithms. As a data science professional, this book will help you design and train better machine learning models to solve a variety of complex problems, and make the machine learn your requirements. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computer algorithms. | |
| 650 | 2 | |a Algorithms | |
| 650 | 2 | |a Machine Learning | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a Mathematical theory of computation. |2 bicssc | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Machine learning. |2 bicssc | |
| 650 | 7 | |a Information architecture. |2 bicssc | |
| 650 | 7 | |a Database design & theory. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computers |x Machine Theory. |2 bisacsh | |
| 650 | 7 | |a Computers |x Data Modeling & Design. |2 bisacsh | |
| 650 | 7 | |a Computer algorithms |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 758 | |i has work: |a MASTERING MACHINE LEARNING ALGORITHMS (Text) |1 https://id.oclc.org/worldcat/entity/E39PCXV3RHdtTwWQj7p966QwRX |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Bonaccorso c/o Quandoo, Giuseppe. |t Mastering Machine Learning Algorithms : Expert techniques to implement popular machine learning algorithms and fine-tune your models. |d Birmingham : Packt Publishing, ©2018 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5405679 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n BDZ0036924762 | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL5405679 | ||
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