Handson Machine Learning with

G?ron, Aur?lien

ISBN 10: 1492032646 / ISBN 13: 9781492032649

Edité par O'Reilly Media, 2019

Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. N° de réf. du vendeur 00101708548

Synopsis :

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. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.

• Explore the machine learning landscape, particularly neural nets
• Use Scikit-Learn to track an example machine-learning project end-to-end
• Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
• Use the TensorFlow library to build and train neural nets
• Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
• Learn techniques for training and scaling deep neural nets

À propos de l'auteur:

Aurélien Géron is a machine learning consultant and trainer. A former Googler, he led YouTube's video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst (a leading Wireless ISP in France) from 2002 to 2012, and a founder and CTO of two consulting firms -- Polyconseil (telecom, media and strategy) and Kiwisoft (machine learning and data privacy).

Détails bibliographiques

Titre : Handson Machine Learning with
Éditeur : O'Reilly Media
Date d'édition : 2019
Reliure : Couverture souple
Etat : Good
Edition : 2ème Édition