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Hands-on machine learning with scikit-learn, keras, and tensorflow : concepts, tools, and techniques to build intelligent systems / Aurelien Geron

By: Material type: TextTextPublication details: Sebastopol, California : O'Reilly Media, Incorporated, c2019Edition: 2nd EditionDescription: xxv, 819 pages : color illustrations ; 24 cmISBN:
  • 9781492032649
Subject(s): LOC classification:
  • Q 325.5 .G47 2019
Contents:
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 -- Neural networks and deep learning -- Introduction to artificial neural networks with Krekas -- Training deep neural networks -- Custom Models and training with TensorFlow -- Loading and Preprocessing data with Tensorflow -- Deep Computer vision using convolutional neutral networks -- Processing Sequences Using RNNs and CNNs -- 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. -- Exercise solutions -- Machine learning project checklist -- SVM dual problem -- Autodiff -- Other popular ANN architectures
Summary: 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.
Item type: Books
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Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Main General Circulation Gen. Ed. - CCIT GC Q 325.5 .G47 2019 (Browse shelf(Opens below)) c.1 Available NULIB000018791

Includes index.

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 -- Neural networks and deep learning -- Introduction to artificial neural networks with Krekas -- Training deep neural networks -- Custom Models and training with TensorFlow -- Loading and Preprocessing data with Tensorflow -- Deep Computer vision using convolutional neutral networks -- Processing Sequences Using RNNs and CNNs -- 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. -- Exercise solutions -- Machine learning project checklist -- SVM dual problem -- Autodiff -- Other popular ANN architectures

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.

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