Completed
Augmenting Neural Nets with a Memory Module
Class Central Classrooms beta
YouTube playlists curated by Class Central.
Classroom Contents
Unsupervised Representation Learning
Automatically move to the next video in the Classroom when playback concludes
- 1 Intro
- 2 Deep Learning = Learning Hierarchical Representations
- 3 Mask R-CNN: instance segmentation
- 4 What is Common Sense?
- 5 Common Sense is the ability to fill in the blanks
- 6 How Much Information Does the Machine Need to
- 7 Training the Actor with Optimized Action Sequences
- 8 Augmenting Neural Nets with a Memory Module
- 9 Memory/Stack-Augmented Recurrent Nets
- 10 Entity Recurrent Neural Net
- 11 Energy-Based Unsupervised Learning
- 12 Seven Strategies to Shape the Energy Function
- 13 constant volume of low energy Energy surface for PCA and K-means 1. build the machine so that the volume of low energy stuff is constant
- 14 use a regularizer that limits do the volume of space that has low energy Sparse coding, sparse auto-encoder, Predictive Sparse Decomposition
- 15 The Hard Part: Prediction Under Uncertainty Invariant prediction: The training samples are merely representatives of a whole set of possible outputs (eg, a manifold of outputs).
- 16 Video Prediction: predicting 5 frames
