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

By: Material type: TextLanguage: English Publisher: Sebastopol : O'Reilly Media, 2022Copyright date: ©2023Edition: Third editionDescription: 834 sidor illustrationer 23 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781098125974
Subject(s): DDC classification:
  • 006.31 23/swe
LOC classification:
  • Q325.5
Contents:
Part I, 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 -- 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 ; Autoencoders, GAN, and diffusion models ; Reinforcement learning ; Training and deploying TensorFlow models at scale ; Machine learning project checklist ; Autodiff ; Special data structures ; TensorFlow graphs.
Holdings
Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
Course book reference Högskolan Väst Entréplan / Entrance floor 006.31 Géron Läses i biblioteket 6004300073555
Course book Högskolan Väst Entréplan / Entrance floor 006.31 Géron Checked out 2026-04-21 6004300073556
Total holds: 0

Part I, 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 -- 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 ; Autoencoders, GAN, and diffusion models ; Reinforcement learning ; Training and deploying TensorFlow models at scale ; Machine learning project checklist ; Autodiff ; Special data structures ; TensorFlow graphs.