applied machine learning von forsyth david (36 Ergebnisse)

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Hardcover. Zustand: new. Hardcover. Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but arent necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing ones own code.A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).Emphasizing the usefulness ofstandard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of: classification using standard machinery (naive bayes; nearest neighbor; SVM) clustering and vector quantization (largely as in PSCS) PCA (largely as in PSCS) variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis) linear regression (largely as in PSCS) generalized linear models including logistic regression model selection with Lasso, elasticnet robustness and m-estimators Markov chains and HMMs (largely as in PSCS) EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once theyve been through that, the next one is easy simple graphical models (in the variational inference section) classification with neural networks, with a particular emphasis onimage classification autoencoding with neural networks structure learning Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Taschenbuch. Zustand: Neu. Applied Machine Learning | David Forsyth | Taschenbuch | xxi | Englisch | 2020 | Springer | EAN 9783030181161 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduatecomputer science programs in machine learning, this textbook is amachine learning toolkit. Applied Machine Learning covers many topicsfor people who want to use machine learning processes to get thingsdone, with a strong emphasis on using existing tools and packages,rather than writing one's own code.A companion to the author'sProbability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).Emphasizing the usefulness ofstandard machinery from appliedstatistics, this textbook gives an overview of the major applied areas inlearning, including coverage of:- classification using standard machinery (naive bayes; nearestneighbor; SVM)- clustering and vector quantization (largely as in PSCS)- PCA (largely as in PSCS)- variants of PCA (NIPALS; latent semantic analysis; canonicalcorrelation analysis)- linear regression (largely as in PSCS)- generalized linear models including logistic regression- model selection with Lasso, elasticnet- robustness and m-estimators- Markov chains and HMM's (largely as in PSCS)- EM in fairly gory detail; long experience teaching this suggests onedetailed example is required, whichstudents hate; but once they've been through that, the next one is easy- simple graphical models (in the variational inference section)- classification with neural networks, with a particular emphasis onimage classification- autoencoding with neural networks- structure learning.

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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 516 | Sprache: Englisch | Produktart: Bücher | 1. Learning to Classify.- 2. SVM's and Random Forests.- 3. A Little Learning Theory.- 4. High-dimensional Data.- 5. Principal Component Analysis.- 6. Low Rank Approximations.- 7. Canonical Correlation Analysis.- 8. Clustering.- 9. Clustering using Probability Models.- 10. Regression.- 11. Regression: Choosing and Managing Models.- 12. Boosting.- 13. Hidden Markov Models.- 14. Learning Sequence Models Discriminatively.- 15. Mean Field Inference.- 16. Simple Neural Networks.- 17. Simple Image Classi¿ers.- 18. Classifying Images and Detecting Objects.- 19. Small Codes for Big Signals.- Index.

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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduatecomputer science programs in machine learning, this textbook is amachine learning toolkit. Applied Machine Learning covers many topicsfor people who want to use machine learning processes to get thingsdone, with a strong emphasis on using existing tools and packages,rather than writing one's own code.A companion to the author'sProbability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).Emphasizing the usefulness ofstandard machinery from appliedstatistics, this textbook gives an overview of the major applied areas inlearning, including coverage of:- classification using standard machinery (naive bayes; nearestneighbor; SVM)- clustering and vector quantization (largely as in PSCS)- PCA (largely as in PSCS)- variants of PCA (NIPALS; latent semantic analysis; canonicalcorrelation analysis)- linear regression (largely as in PSCS)- generalized linear models including logistic regression- model selection with Lasso, elasticnet- robustness and m-estimators- Markov chains and HMM's (largely as in PSCS)- EM in fairly gory detail; long experience teaching this suggests onedetailed example is required, whichstudents hate; but once they've been through that, the next one is easy- simple graphical models (in the variational inference section)- classification with neural networks, with a particular emphasis onimage classification- autoencoding with neural networks- structure learning.

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Hardcover. Zustand: Brand New. 494 pages. 11.00x8.50x1.25 inches. In Stock.

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Hardcover. Zustand: new. Hardcover. Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but arent necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing ones own code.A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).Emphasizing the usefulness ofstandard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of: classification using standard machinery (naive bayes; nearest neighbor; SVM) clustering and vector quantization (largely as in PSCS) PCA (largely as in PSCS) variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis) linear regression (largely as in PSCS) generalized linear models including logistic regression model selection with Lasso, elasticnet robustness and m-estimators Markov chains and HMMs (largely as in PSCS) EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once theyve been through that, the next one is easy simple graphical models (in the variational inference section) classification with neural networks, with a particular emphasis onimage classification autoencoding with neural networks structure learning Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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paperback. Zustand: New. Language:Chinese.Paperback. Pub Date: 2021-01-01 Pages: 348 Publisher: Machinery Industry Press This textbook is a machine learning toolbox. suitable for the fourth-year undergraduate students or the first-year graduate students in computer science.?This book provides many topics for those who want to use the machine learning process to accomplish tasks. emphasizing the use of existing tools and packages instead of rewriting the code yourself.?This book is suitable for teaching or reading fro.

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduatecomputer science programs in machine learning, this textbook is amachine learning toolkit. Applied Machine Learning covers many topicsfor people who want to use machine learning processes to get thingsdone, with a strong emphasis on using existing tools and packages,rather than writing one's own code.A companion to the author'sProbability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).Emphasizing the usefulness ofstandard machinery from appliedstatistics, this textbook gives an overview of the major applied areas inlearning, including coverage of:- classification using standard machinery (naive bayes; nearestneighbor; SVM)- clustering and vector quantization (largely as in PSCS)- PCA (largely as in PSCS)- variants of PCA (NIPALS; latent semantic analysis; canonicalcorrelation analysis)- linear regression (largely as in PSCS)- generalized linear models including logistic regression- model selection with Lasso, elasticnet- robustness and m-estimators- Markov chains and HMM's (largely as in PSCS)- EM in fairly gory detail; long experience teaching this suggests onedetailed example is required, whichstudents hate; but once they've been through that, the next one is easy- simple graphical models (in the variational inference section)- classification with neural networks, with a particular emphasis onimage classification- autoencoding with neural networks- structure learning 516 pp. Englisch.