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Neural Networks: Tricks of the Trade
Deep Learning via Semi-supervised Embedding
other
Author(s):
Jason Weston
,
Frédéric Ratle
,
Hossein Mobahi
,
Ronan Collobert
Publication date
(Print):
2012
Publisher:
Springer Berlin Heidelberg
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There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.
Related collections
Computer Vision, Deep Learning, Deep Reinforcement Learning, IoT
Author and book information
Book Chapter
Publication date (Print):
2012
Pages
: 639-655
DOI:
10.1007/978-3-642-35289-8_34
SO-VID:
ae7a5cbd-e482-4a51-b769-7948ac9814bb
License:
http://www.springer.com/tdm
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Book chapters
pp. 9
Efficient BackProp
pp. 295
Neural Network Classification and Prior Class Probabilities
pp. 53
Early Stopping — But When?
pp. 235
Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagation
pp. 421
Stochastic Gradient Descent Tricks
pp. 437
Practical Recommendations for Gradient-Based Training of Deep Architectures
pp. 479
Training Deep and Recurrent Networks with Hessian-Free Optimization
pp. 561
Learning Feature Representations with K-Means
pp. 599
A Practical Guide to Training Restricted Boltzmann Machines
pp. 639
Deep Learning via Semi-supervised Embedding
pp. 659
A Practical Guide to Applying Echo State Networks
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