DeepLearning.AI - Sequence Models
4.7 /5- Offered byCoursera
Sequence Models at Coursera Overview
Duration | 16 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Sequence Models at Coursera Highlights
- 38% started a new career after completing these courses.
- 39% got a tangible career benefit from this course.
- 13% got a pay increase or promotion.
Sequence Models at Coursera Course details
- This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.
- You will:
- - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
- - Be able to apply sequence models to natural language problems, including text synthesis.
- - Be able to apply sequence models to audio applications, including speech recognition and music synthesis.
- This is the fifth and final course of the Deep Learning Specialization.
- deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.
Sequence Models at Coursera Curriculum
Recurrent Neural Networks
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
Gated Recurrent Unit (GRU) *CORRECTION*
Long Short Term Memory (LSTM) *CORRECTION*
Recurrent Neural Networks
Natural Language Processing & Word Embeddings
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
GloVe word vectors *CORRECTION*
Natural Language Processing & Word Embeddings
Sequence models & Attention mechanism
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
Conclusion and thank you
Bleu Score *CORRECTION*
Corrections
Workera's Standardized Tests for AI Skills
Instructions if you are unable to open your notebook
Sequence models & Attention mechanism
Other courses offered by Coursera
Student Forum
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