Sequence Models by Coursera
Skills you will learn!
Curriculum
21 Topics
Why Sequence Models?
Notation
Recurrent Neural Network Model
Backpropagation Through Time
Different Types of RNNs
Language Model and Sequence Generation
Sampling Novel Sequences
Vanishing Gradients with RNNs
Gated Recurrent Unit (GRU)
Long Short Term Memory (LSTM)
Bidirectional RNN
Deep RNNs
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Clarifications about Upcoming Gated Recurrent Unit (GRU) Video
Clarifications about Upcoming Long Short Term Memory (LSTM) Video
Lecture Notes W1
(Optional) Downloading your Notebook Downloading your Workspace and Refreshing your Workspace
Recurrent Neural Networks
Building your Recurrent Neural Network - Step by Step
Dinosaur Island-Character-Level Language Modeling
Jazz Improvisation with LSTM
15 Topics
Word Representation
Using Word Embeddings
Properties of Word Embeddings
Embedding Matrix
Learning Word Embeddings
Word2Vec
Negative Sampling
GloVe Word Vectors
Sentiment Classification
Debiasing Word Embeddings
Clarifications about Upcoming GloVe Word Vectors Video
Lecture Notes W2
Natural Language Processing & Word Embeddings
Operations on Word Vectors - Debiasing
Emojify
16 Topics
Basic Models
Picking the Most Likely Sentence
Beam Search
Refinements to Beam Search
Error Analysis in Beam Search
Bleu Score (Optional)
Attention Model Intuition
Attention Model
Speech Recognition
Trigger Word Detection
Clarifications about Upcoming Attention Model Video
Lecture Notes W3
Instructions If You Are Unable to Open Your Notebook
Sequence Models & Attention Mechanism
Neural Machine Translation
Trigger Word Detection
15 Topics
Transformer Network Intuition
Self-Attention
Multi-Head Attention
Transformer Network
Conclusion and Thank You!
Lecture Notes W4
[IMPORTANT] Reminder about end of access to Lab Notebooks
References
Acknowledgments
(Optional) Opportunity to Mentor Other Learners
Transformers
Transformers Architecture with TensorFlow
Transformer Pre-processing
Transformer Network Application: Named-Entity Recognition
Transformer Network Application: Question Answering
Sequence Models
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