'Deep networks' presentation slideshows

Structure learning with deep neuronal networks

813 views • 75 slides

Deep Learning

Deep Learning. Early Work Why Deep Learning Stacked Auto Encoders Deep Belief Networks. Deep Learning Overview. Train networks with many layers (vs. shallow nets with just a couple of layers) Multiple layers work to build an improved feature space

577 views • 34 slides

ALTERNATE LAYER SPARSITY & INTERMEDIATE FINE-TUNING FOR DEEP AUTOENCODERS

Submitted by: Supervised by: Ankit Bhutani Prof. Amitabha Mukerjee (Y9227094) Prof. K S Venkatesh. ALTERNATE LAYER SPARSITY & INTERMEDIATE FINE-TUNING FOR DEEP AUTOENCODERS. AUTOENCODERS. AUTO-ASSOCIATIVE NEURAL NETWORKS OUTPUT SIMILAR AS INPUT. DIMENSIONALITY REDUCTION.

446 views • 33 slides

Projects

Projects. Registration Home work Latex Team Projects. Term papers/Projects proposals are due on Tuesday 10/23/12 Within a week we will give you an approval to continue with your project along with comments and/or a request to modify/augment/do a different project .

467 views • 33 slides

Deep Learning

Deep Learning. Early Work Why Deep Learning Stacked Auto Encoders Deep Belief Networks. Deep Learning Overview. Train networks with many layers (vs. shallow nets with just a couple of layers) Multiple layers work to build an improved feature space

344 views • 33 slides

Recent Developments in Deep Learning

Recent Developments in Deep Learning. Quoc V. Le Stanford University and Google. Purely supervised. Quoc V. Le. Deep Learning. Almost abandoned between 2000-2006 - Overfitting , slow, many local minima, gradient vanishing

537 views • 28 slides

Learning in Recurrent Networks

Learning in Recurrent Networks. Psychology 209 February 25, 2013. Outline. Back Propagation through time Alternatives that can teach networks to settle to fixed points Learning conditional distributions An application Collaboration of hippocampus & cortex in learning new associations.

339 views • 22 slides

Learning in Recurrent Networks

Learning in Recurrent Networks. Psychology 209 February 25, 2013. Outline. Back Propagation through time Alternatives that can teach networks to settle to fixed points Learning conditional distributions An application Collaboration of hippocampus & cortex in learning new associations.

360 views • 22 slides

Biology Scholars Program biologyscholars

Biology Scholars Program www.biologyscholars.org. American Society for Microbiology (ASM) Washington, DC. BSP2 Goals (2011-2015). Empower biologists to be models of scholarly teaching and practitioners of science education research.

180 views • 10 slides

86 views • 8 slides