Deep Learning with Theano: Perform large-scale numerical and scientific computations efficiently
Language: English
Published by Packt Publishing, 2017
- Softcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
AbeBooks seller since March 25, 2015
Condition: New
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Add to basketItem description from seller
In English.
Seller Inventory # ria9781786465825_new
- Title
- Deep Learning with Theano: Perform large-scale numerical and scientific computations efficiently
- Author
- Bourez, Christopher
- Publisher
- Packt Publishing
- Publication year
- 2017
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1786465825
- ISBN 13
- 9781786465825
- Item weight
- 640 grams
"Synopsis" may belong to another edition of this title.
About the Author
Christopher Bourez graduated from Ecole Polytechnique and Ecole Normale Supérieure de Cachan in Paris in 2005 with a Master of Science in Math, Machine Learning and Computer Vision (MVA).
For 7 years, he led a company in computer vision that launched Pixee, a visual recognition application for iPhone in 2007, with the major movie theater brand, the city of Paris and the major ticket broker: with a snap of a picture, the user could get information about events, products, and access to purchase.
While working on missions in computer vision with Caffe, TensorFlow or Torch, he helped other developers succeed by writing on a blog on computer science. One of his blog posts, a tutorial on the Caffe deep learning technology, has become the most successful tutorial on the web after the official Caffe website.
On the initiative of Packt Publishing, the same recipes that made the success of his Caffe tutorial have been ported to write this book on Theano technology. In the meantime, a wide range of problems for Deep Learning are studied to gain more practice with Theano and its application.
"About the title" may belong to another edition of this title.
Ria Christie Collections
Uxbridge, United Kingdom
AbeBooks seller since March 25, 2015
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