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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: TextTextPublication details: Sebastapol, CA, O'Reilly Media, Inc., 2023.Edition: 3rd edDescription: xxv, 834 p.: ill. (some col.) ; 24 cmISBN:
  • 9781098125974
Subject(s): DDC classification:
  • 005.133 23
LOC classification:
  • QA76.73  G47 2023
Contents:
The fundamentals of machine learning. The machine learning landscape--End-to-end machine learning project--Classification--Training model--Support vector machines--Decision trees ; Ensemble learning and random forests--Dimensionality reduction--Unsupervised learning techniques--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, GANs, and diffusion models--Reinforcement learning--Training and deploying TensorFlow models at scale.
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Holdings
Item type Current library Collection Call number Status Date due Barcode
Books Books Zetech Library - Mang'u General Stacks Non-fiction QA76.73 .G47 2023 (Browse shelf(Opens below)) Available Z011571

Includes bibliographical references and index.

The fundamentals of machine learning. The machine learning landscape--End-to-end machine learning project--Classification--Training model--Support vector machines--Decision trees ; Ensemble learning and random forests--Dimensionality reduction--Unsupervised learning techniques--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, GANs, and diffusion models--Reinforcement learning--Training and deploying TensorFlow models at scale.

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