In this presentation, I would like to review basis techniques, models, and applications in deep learning. Hope you find the slides are interesting. Further information about my research can be found at "https://sites.google.com/site/ihaiphan/."
NhatHai Phan
CIS Department,
University of Oregon, Eugene, OR
convolutional neural network and its applications.pdf
Tutorial on Deep learning and Applications
1. Tutorial on Deep Learning and
Applications
Hai Phan
AIM Lab, University of Oregon
1Includes slide material sourced from Yoshua Bengio, Geoff Hinton, Yann LeCun, Andrew Ng, Honglak Lee, and
Marc’Aurelio Ranzato
2. Outline
• Deep learning
– Greedy level-wise training (supervised learning)
– Restricted Boltzmann machine (RBM)
– Deep belief networks
– Stacked autoassociators
– Deep Boltzmann machines
• Applications
– Human motion modeling
– Vision
– Language
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3. Motivation: why go deep?
• Deep Architectures can be representationally
efficient
• Deep representation might allow for a
hierarchy of representations
– Comprehensibility
• Multiple levels of latent variables allow
combinatorial sharing of statistical strength
• Deep architectures work well (vision, audio,
NLP, etc.)!
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