Completed
What can LSTMs Learn? (2) (Shi et al. 2016, Radford et al. 2017) Count length of sentence
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Neural Nets for NLP - Recurrent Networks for Sentence or Language Modeling
Automatically move to the next video in the Classroom when playback concludes
- 1 Intro
- 2 Why Model Sentence Pairs?
- 3 Siamese Network (Bromley et al. 1993)
- 4 Convolutional Matching Model (Hu et al. 2014) • Concatenate sentences into a 30 tensor and perform convolution
- 5 Convolutional Features + Matrix-based Pooling in and Schutze 2015
- 6 NLP and Sequential Data
- 7 Long-distance Dependencies in Language
- 8 Can be Complicated!
- 9 Recurrent Neural Networks (Elman 1990)
- 10 Unrolling in Time • What does processing a sequence look like?
- 11 What Can RNNs Do?
- 12 Representing Sentences
- 13 e.g. Language Modeling
- 14 RNNLM Example: Loss Calculation and State Update
- 15 Vanishing Gradient • Gradients decrease as they get pushed back
- 16 LSTM Structure
- 17 What can LSTMs Learn? (2) (Shi et al. 2016, Radford et al. 2017) Count length of sentence
- 18 Handling Long Sequences
