Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e

Aurelien Geron

ISBN 10: 1098125975 / ISBN 13: 9781098125974

Verlag: O'Reilly Media, US, 2022

Beschreibung

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 best-selling 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 Aurelien Geron 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 machine learning project end to endExplore several models, including support vector machines, decision trees, random forests, and ensemble methodsExploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detectionDive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, and transformersUse TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learningTrain neural nets using multiple GPUs and deploy them at scale using Google's Vertex AI.

Bestandsnummer des Verkäufers LU-9781098125974

Bibliografische Details

• Titel: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e
• Verlag: O'Reilly Media, US
• Erscheinungsjahr: 2022
• Sprache: Englisch
• ISBN-10: 1098125975
• ISBN-13: 9781098125974
• Einband: Paperback
• Auflage: 3. Auflage
• Zustand: New

Über diesen Titel

Inhaltsangabe

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

Über die Autorin bzw. den Autor

Aurelien Geron is a Machine Learning consultant. A former Googler, he led YouTube's video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, a telecom consulting firm. Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Societe Generale), defense (Canada's DOD), and healthcare (blood transfusion). He published a few technical books (on C++, WiFi, and Internet architectures), and was a Computer Science lecturer in a French engineering school. A few fun facts: he taught his 3 children to count in binary with their fingers (up to 1023), he studied microbiology and evolutionary genetics before going into software engineering, and his parachute didn't open on the 2nd jump.