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Deep Learning with Theano

You're reading from  Deep Learning with Theano

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781786465825
Pages 300 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Christopher Bourez Christopher Bourez
Profile icon Christopher Bourez
LinkedIn
Christopher Bourez graduated from Ecole Polytechnique and Ecole Normale Suprieure 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.
See other products by Christopher Bourez

Table of Contents (22) Chapters

Deep Learning with Theano
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Theano Basics Classifying Handwritten Digits with a Feedforward Network Encoding Word into Vector Generating Text with a Recurrent Neural Net Analyzing Sentiment with a Bidirectional LSTM Locating with Spatial Transformer Networks Classifying Images with Residual Networks Translating and Explaining with Encoding – decoding Networks Selecting Relevant Inputs or Memories with the Mechanism of Attention Predicting Times Sequences with Advanced RNN Learning from the Environment with Reinforcement Learning Features with Unsupervised Generative Networks Extending Deep Learning with Theano Index

Multimodal deep learning

To open the possible applications further, the encoding-decoding framework can be applied with different modalities, such as, for example, for image captioning.

Image captioning consists of describing the content of the image with words. The input is an image, naturally encoded into a thought vector with a deep convolutional network.

The text to describe the content of the image can be produced from this internal state vector with the same stack of LSTM networks as a decoder, as in Seq2seq networks:

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Authors (1)

Profile icon Christopher Bourez
LinkedIn
Christopher Bourez graduated from Ecole Polytechnique and Ecole Normale Suprieure 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.
Read more
See other products by Christopher Bourez

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