About this Course

210,101 recent views
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level

We recommend that you have taken the first two courses of the Natural Language Processing Specialization, offered by deeplearning.ai

Approx. 19 hours to complete
English

What you will learn

  • Create word embeddings, then train a neural network on them to perform sentiment analysis of tweets

  • Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model

  • Train a recurrent neural network to extract important information from text, using named entity recognition (NER) and LSTMs with linear layers

  • Use a Siamese network to compare questions in a text and identify duplicates: questions that are worded differently but have the same meaning

Skills you will gain

Word EmbeddingSentiment with Neural NetsSiamese NetworksNatural Language GenerationNamed-Entity Recognition
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level

We recommend that you have taken the first two courses of the Natural Language Processing Specialization, offered by deeplearning.ai

Approx. 19 hours to complete
English

Offered by

Placeholder

DeepLearning.AI

Syllabus - What you will learn from this course

Content RatingThumbs Up94%(1,213 ratings)Info
Week
1

Week 1

5 hours to complete

Neural Networks for Sentiment Analysis

5 hours to complete
9 videos (Total 35 min), 3 readings, 1 quiz
9 videos
Neural Networks for Sentiment Analysis3m
Trax: Neural Networks2m
Why we recommend Trax13m
Trax: Layers3m
Dense and ReLU Layers1m
Serial Layer1m
Other Layers 3m
Training2m
3 readings
Connect with your mentors and fellow learners on Slack!10m
Reading: (Optional) Trax and JAX, docs and code15m
How to Refresh your Workspace10m
Week
2

Week 2

5 hours to complete

Recurrent Neural Networks for Language Modeling

5 hours to complete
8 videos (Total 27 min)
8 videos
Recurrent Neural Networks4m
Applications of RNNs3m
Math in Simple RNNs3m
Cost Function for RNNs1m
Implementation Note 2m
Gated Recurrent Units4m
Deep and Bi-directional RNNs 3m
Week
3

Week 3

4 hours to complete

LSTMs and Named Entity Recognition

4 hours to complete
6 videos (Total 24 min), 3 readings, 1 quiz
6 videos
Introduction to LSTMs4m
LSTM Architecture3m
Introduction to Named Entity Recognition3m
Training NERs: Data Processing 4m
Computing Accuracy1m
3 readings
(Optional) Intro to optimization in deep learning: Gradient Descent10m
(Optional) Understanding LSTMs10m
Long Short-Term Memory (Deep Learning Specialization C5)10m
Week
4

Week 4

5 hours to complete

Siamese Networks

5 hours to complete
8 videos (Total 33 min), 1 reading, 1 quiz
8 videos
Architecture3m
Cost Function3m
Triplets6m
Computing The Cost I5m
Computing The Cost II6m
One Shot Learning2m
Training / Testing3m
1 reading
Acknowledgments10m

Reviews

TOP REVIEWS FROM NATURAL LANGUAGE PROCESSING WITH SEQUENCE MODELS

View all reviews

About the Natural Language Processing Specialization

Natural Language Processing

Frequently Asked Questions

More questions? Visit the Learner Help Center.