Hands-on machine learning with scikit-learn and TensorFlow : concepts, tools and techniques to build intelligent systems /
Géron, Aurélien.
Hands-on machine learning with scikit-learn and TensorFlow : concepts, tools and techniques to build intelligent systems / Aurélien Géron ; editor Nicole Tache ; interior designer David Futato ; cover designer Randy Comer ; illustrator Rebeca Demarest. - First editions. - Beijing ; Boston : O'Reilly Media, 2017. - xx, 551 páginas : ilustraciones, gráficas, tablas a blanco y negro ; 23 cm.
Material de apoyo del Departamento de Sistemas y Tecnología.
Preface, xiii -- Part I. The fundamentals of machine learning -- 1. The machine learning landscape, 3 -- 2. End-to-end machine learning project, 33 -- 3. Classification, 81 -- 4. Training models, 107 -- 5. Support vector machines, 147 -- 6. Decision trees, 169 -- 7. Ensemble learning and Random Forests, 183 -- 8. Dimensionality reduction, 207 -- Part II. Neural networks and deep learning, 231 -- 10. Introduction to artificial neural networks, 257 -- 11. Training deep neural nets, 279 -- 12. Distributing TensorFklow across devices and servers, 319 -- 13. Convolutional neural networks, 361 -- 14. Recurrent neural networks, 387 -- 15. Autoencoders, 421 -- 16. Reinforcement learning, 447 -- A. Exercise solutions, 481 -- B. Machine learning project checklist, 507 -- C. SVM Dual problem, 513 -- D. Autodiff, 517 -- E. Other popular ann architectures, 525 -- Index, 535.
9781491962299
Inteligencia artificial.
Q 325 .5 / .G47 2017
Hands-on machine learning with scikit-learn and TensorFlow : concepts, tools and techniques to build intelligent systems / Aurélien Géron ; editor Nicole Tache ; interior designer David Futato ; cover designer Randy Comer ; illustrator Rebeca Demarest. - First editions. - Beijing ; Boston : O'Reilly Media, 2017. - xx, 551 páginas : ilustraciones, gráficas, tablas a blanco y negro ; 23 cm.
Material de apoyo del Departamento de Sistemas y Tecnología.
Preface, xiii -- Part I. The fundamentals of machine learning -- 1. The machine learning landscape, 3 -- 2. End-to-end machine learning project, 33 -- 3. Classification, 81 -- 4. Training models, 107 -- 5. Support vector machines, 147 -- 6. Decision trees, 169 -- 7. Ensemble learning and Random Forests, 183 -- 8. Dimensionality reduction, 207 -- Part II. Neural networks and deep learning, 231 -- 10. Introduction to artificial neural networks, 257 -- 11. Training deep neural nets, 279 -- 12. Distributing TensorFklow across devices and servers, 319 -- 13. Convolutional neural networks, 361 -- 14. Recurrent neural networks, 387 -- 15. Autoencoders, 421 -- 16. Reinforcement learning, 447 -- A. Exercise solutions, 481 -- B. Machine learning project checklist, 507 -- C. SVM Dual problem, 513 -- D. Autodiff, 517 -- E. Other popular ann architectures, 525 -- Index, 535.
9781491962299
Inteligencia artificial.
Q 325 .5 / .G47 2017