Completed
Single Layer Cost Function
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Regularization of Big Neural Networks
Automatically move to the next video in the Classroom when playback concludes
- 1 Intro
- 2 Big Neural Nets
- 3 Big Models Over-Fitting
- 4 Training with DropOut
- 5 DropOut/Connect Intuition
- 6 Theoretical Analysis of DropConnect
- 7 MNIST Results
- 8 Varying Size of Network
- 9 Varying Fraction Dropped
- 10 Comparison of Convergence Rates
- 11 Limitations of DropOut/Connect
- 12 Stochastic Pooling
- 13 Methods for Test Time
- 14 Varying Size of Training Set
- 15 Convergence / Over-Fitting
- 16 Street View House Numbers
- 17 Deconvolutional Networks
- 18 Recap: Sparse Coding (Patch-based)
- 19 Reversible Max Pooling
- 20 Single Layer Cost Function
- 21 Single Layer Inference
- 22 Effect of Sparsity
- 23 Effect of Pooling Variables
- 24 Talk Overview
- 25 Stacking the Layers
- 26 Two Layer Example
- 27 Link to Parts and Structure Models
- 28 Caltech 101 Experiments
- 29 Layer 2 Filters
- 30 Classification Results: Caltech 101
- 31 Deconvolutional + Convolutional
- 32 Summary
