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Hands-On Machine Learning with - Taschenbuch, von Géron Aurélien - Neu h-
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eBay-Artikelnr.:126977388526
Artikelmerkmale
- Artikelzustand
- Book Title
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlo
- ISBN
- 9781098125974
Über dieses Produkt
Product Identifiers
Publisher
O'reilly Media, Incorporated
ISBN-10
1098125975
ISBN-13
9781098125974
eBay Product ID (ePID)
19057257302
Product Key Features
Number of Pages
861 Pages
Publication Name
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
Language
English
Subject
Intelligence (Ai) & Semantics, Natural Language Processing, Neural Networks, Data Processing, Programming Languages / Python, Computer Vision & Pattern Recognition
Publication Year
2022
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Height
1.8 in
Item Weight
51 Oz
Item Length
9.1 in
Item Width
7.6 in
Additional Product Features
Edition Number
3
Intended Audience
Scholarly & Professional
LCCN
2023-549175
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
Synopsis
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 Aurélien Géron 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
LC Classification Number
Q325.5.G47 2022
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