Hands—On Machine Learning with Scikit—Learn and TensorFlow. Este artículo no está disponible.
Idioma: inglés
Editorial: O'Reilly Media, 2017
- Tapa blanda
- Usado

Librería: WorldofBooks, Goring-By-Sea, WS, Reino UnidoWorldofBooks
Vendedor de AbeBooks desde el 16 de marzo de 2007
Condición: Usado - Bueno
EUR 4,54
Descripción del artículo del vendedor
The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
N° de ref. del artículo GOR009031172
- Título
- Hands—On Machine Learning with Scikit—Learn and TensorFlow
- Autor
- Geron, Aurelien
- Editorial
- O'Reilly Media
- Año de publicación
- 2017
- Estado
- Very Good
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 1491962291
- ISBN 13
- 9781491962299
- Peso del artículo
- 916 gramos
Graphics in this book are printed in black and white.
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
- Apply practical code examples without acquiring excessive machine learning theory or algorithm details
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Aurélien Géron is a Machine Learning consultant. A former Googler, he led the YouTube 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, the firm that now manages the electric car sharing service Autolib'.Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), 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.
“Acerca de” puede pertenecer a otra edición de este título.