Completed
A Unified View of Sequence- to-sequence Models
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
CMU Multilingual NLP - Machine Translation-Sequence-to-Sequence Models
Automatically move to the next video in the Classroom when playback concludes
- 1 Intro
- 2 Language Models • Language models are generative models of text
- 3 Conditioned Language Models
- 4 Calculating the Probability of a Sentence
- 5 Conditional Language Models
- 6 One Type of Language Model Mikolov et al. 2011
- 7 How to Pass Hidden State?
- 8 The Generation Problem
- 9 Ancestral Sampling
- 10 Greedy Search
- 11 Beam Search
- 12 Sentence Representations
- 13 Calculating Attention (1)
- 14 A Graphical Example
- 15 Attention Score Functions (1)
- 16 Attention is not Alignment! (Koehn and Knowles 2017)
- 17 Coverage
- 18 Multi-headed Attention
- 19 Supervised Training (Liu et al. 2016)
- 20 Self Attention (Cheng et al. 2016) • Each element in the sentence attends to other
- 21 Why Self Attention?
- 22 Transformer Attention Tricks
- 23 Transformer Training Tricks
- 24 Masking for Training . We want to perform training in as few operations as possible using big matrix multiplies
- 25 A Unified View of Sequence- to-sequence Models
- 26 Code Walk
