Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems /

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Detalles Bibliográficos
Autor principal: Géron, Aurélien
Formato: Libro
Lenguaje:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, 2019
Edición:2nd ed.
Materias:
Inteligencia artificial
Aprendizaje automático
CIBERNETICA
REDES NEURONALES ARTIFICIALES
APRENDIZAJE PROFUNDO
Acceso en línea:Tabla de contenidos extendida
Fe de erratas (último acceso: 5/8/2022)
Aporte de:Registro referencial: Solicitar el recurso aquí
Biblioteca E.A. Barbagelata (FI-UNER) de Universidad Nacional de Entre Ríos
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245 1 0 |a Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow :  |b concepts, tools, and techniques to build intelligent systems /  |c Aurélien Géron 
250 |a 2nd ed. 
260 2 |a Sebastopol, CA :  |b O'Reilly Media,  |c 2019 
300 |a xxv, 819 p. :  |b fig. byn. ;  |c 24 cm. 
500 |a Frameworks de Python utilizadas en este libro: Scikit-Learn (https://scikit-learn.org/stable/); TensorFlow (https://www.tensorflow.org/); https://keras.io/ 
500 |a Se asume el conocimiento de librerías científicas tales como: NumPy (https://numpy.org/); Pandas (https://pandas.pydata.org/) y Matplotlib (https://matplotlib.org/) 
505 |a Preface -- I. The Fundamentals of Machine Learning: 1. The Machine Learning Landscape -- 2. End-to-End Machine Learning Project -- 3. Classification -- 4. Training Models -- 5. Support Vector Machines -- 6. Decision trees -- 7. Ensemble Learning and Random Forests -- 8. Dimensionality Reduction -- 9. Unsupervised Learning Techniques -- II. Neural Networks and Deep Learning: 10. Introduction to Artificial Neural Networks with Keras -- 11. Training Deep Neural Networks -- 12. Custom Models and Training with TensorFlow -- 13. Loading and Preprocessing Data with TensorFlow -- 14. Deep Computer Vision Using Convolutional Neural Networks -- 15. Processing Sequences Using RNNs and CNNs -- 16. Natural Language processing with RNNs and Attention -- 17. Representation Learning and Generative Learning Using Autoencoders and GANs -- 18. Reinforcement Learning -- 19. Training and Deploying TensorFlow Models at Scale -- A. Exercise Solutions -- B. Machine Learning Project Checklist -- C. SVM Dual Problem -- D. Autodiff -- E. Other Popular ANN Architectures -- F. Special Data Structures -- G. TensorFlow Graphs -- Index 
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