Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems / Aurélien Géron.
Material type: TextLanguage: English Publisher: California : O'Reilly Media, Inc., 2017Description: xx, 544 páginas ; 24 cmContent type: texto Media type: sin mediación Carrier type: volumenISBN: 9781491962299 Subject(s): Programación funcional (Computadores) | Aprendizaje automático (Inteligencia artificial) | Inteligencia artificial | Python (lenguaje de programación de computadores) | Simulación por computadores | Procesamiento de datosDDC classification: 006.31Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
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The fundamentals of machine learning. The machine learning landscape ; End-to-end machine learning project ; Classification ; Training models ; Support vector machines ; Decision trees ; Ensemble learning and random forests ; Dimensionality reduction ; Unsupervised learning techniques -- |g Part II, Neural networks and deep learning. Introduction to artificial neural networks with Keras ; Training deep neural networks ; Custom models and training with TensorFlow ; Loading and preprocessing data with TensorFlow ; Deep computer vision using convolutional neural networks ; Processing sequences using RNNs and CNNs ; Natural language processing with RNNs and attention ; Representation learning and generative learning using autoencoders and GANs ; Reinforcement learning ; Training and deploying TensorFlow models at scale ; Exercise solutions ; Machine learning project checklist ; SVM dual problem ; Autodiff ; Other popular ANN architectures ; Special data structures ; TensorFlow graphs.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job.
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