Hands-on machine learning with Scikit-Learn, Keras, and Tensorflow : concepts, tools, and techniques to build intelligent systems / Aurelien Geron.
Material type: TextPublication details: Sebastopol, California : O'Reilly Media, Inc., ©2022Edition: Third editionDescription: xxv, 834 pages : illustrations (chiefly color) ; 24 cmISBN: 9781098122478 (paperback); 109812247XSubject(s): TensorFlow | Machine learning | Artificial intelligence | Python (Computer program language)DDC classification: 006.3/1 LOC classification: Q325.5 | .G476 2023 c.1Summary: Through a recent series of 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. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aur©♭lien G©♭ron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learningItem type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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General Circulation | APU Library Open Shelf | Book | Q325.5 .G476 2023 c.1 (Browse shelf (Opens below)) | 1 | Available | 00013087 |
Browsing APU Library shelves, Shelving location: Open Shelf, Collection: Book Close shelf browser (Hides shelf browser)
Q325.5 .B69 2015 c. 1 Machine learning in Python : | Q325.5 .C48 2018 c.1 Machine learning and security : protecting systems with data and algorithms / | Q325.5 .G46 2019 c.1 Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : | Q325.5 .G476 2023 c.1 Hands-on machine learning with Scikit-Learn, Keras, and Tensorflow : concepts, tools, and techniques to build intelligent systems / | Q325.5 .G66 2016 c.1 Deep learning / | Q325.5 .M33 1998 c.1 Machine learning and data mining : | Q325.5 .M37 2015 c.1 Machine learning : |
Includes index
Through a recent series of 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. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aur©♭lien G©♭ron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
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