DeepLearning.AI - Natural Language Processing with Sequence Models
- Offered byCoursera
Natural Language Processing with Sequence Models at Coursera Overview
Duration | 19 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Natural Language Processing with Sequence Models at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Natural Language Processing with Sequence Models at Coursera Course details
- In Course 3 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:
- a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets,
- b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model,
- c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and
- d) Use so-called ?Siamese? LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning.
- Please make sure that you?ve completed Course 2 and are familiar with the basics of TensorFlow. If you?d like to prepare additionally, you can take Course 1: Neural Networks and Deep Learning of the Deep Learning Specialization.
- By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot!
- This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. ?ukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
Natural Language Processing with Sequence Models at Coursera Curriculum
Neural Networks for Sentiment Analysis
Course 3 Introduction
Neural Networks for Sentiment Analysis
Trax: Neural Networks
Why we recommend Trax
Trax: Layers
Dense and ReLU Layers
Serial Layer
Other Layers
Training
Connect with your mentors and fellow learners on Slack!
Reading: (Optional) Trax and JAX, docs and code
How to Refresh your Workspace
Recurrent Neural Networks for Language Modeling
Traditional Language models
Recurrent Neural Networks
Applications of RNNs
Math in Simple RNNs
Cost Function for RNNs
Implementation Note
Gated Recurrent Units
Deep and Bi-directional RNNs
LSTMs and Named Entity Recognition
RNNs and Vanishing Gradients
Introduction to LSTMs
LSTM Architecture
Introduction to Named Entity Recognition
Training NERs: Data Processing
Computing Accuracy
(Optional) Intro to optimization in deep learning: Gradient Descent
(Optional) Understanding LSTMs
Long Short-Term Memory (Deep Learning Specialization C5)
Siamese Networks
Siamese Networks
Architecture
Cost Function
Triplets
Computing The Cost I
Computing The Cost II
One Shot Learning
Training / Testing
Acknowledgments
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