Natural Language Processing in TensorFlow
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the DeepLearning.AI TensorFlow Developer Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network.
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- Level Professional
- المدة 24 ساعات hours
- الطبع بواسطة DeepLearning.AI
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If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You'll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you'll get to train an LSTM on existing text to create original poetry! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.الوحدات
Introduction
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1
Videos
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1
Readings
1
Videos
- Introduction: A conversation with Andrew Ng
1
Readings
- Welcome to the course!
Sentiment in text
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12
Videos
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3
Readings
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3
Lab
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1
Assignment
1
Assignment
- Week 1 Quiz
3
Labs
- Check out the code! (Lab 1)
- Check out the code! (Lab 2)
- Check out the code! (Lab 3)
12
Videos
- Introduction
- Word based encodings
- Using APIs
- Notebook for lesson 1
- Text to sequence
- Padding
- Out-of-Vocabulary Words
- Notebook for lesson 2
- Sarcasm, really?
- Preprocessing the Sarcasm dataset
- Notebook for lesson 3
- Week 1 Wrap up
3
Readings
- [IMPORTANT] Have questions, issues or ideas? Join our Forum!
- About the notebooks in this course
- News headlines dataset for sarcasm detection
Lecture Notes (Optional)
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1
Readings
1
Readings
- Lecture Notes Week 1
Weekly Assignment - Explore the BBC News Archive
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2
Readings
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1
Programming
- Explore the BBC news archive
2
Readings
- Assignment Troubleshooting Tips
- (Optional) Downloading your Notebook and Refreshing your Workspace
Word Embeddings
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12
Videos
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3
Readings
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3
Lab
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1
Assignment
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Assignment
- Week 2 Quiz
3
Labs
- Check out the code! (Lab 1)
- Check out the code! (Lab 2)
- Check out the code! (Lab 3)
12
Videos
- A conversation with Andrew Ng
- Introduction
- The IMDB dataset
- Looking into the details
- How can we use vectors?
- More into the details
- Notebook for lesson 1
- Remember the sarcasm dataset?
- Building a classifier for the sarcasm dataset
- Let’s talk about the loss
- Subword tokenization
- Diving into the code
3
Readings
- IMDB reviews dataset
- Subword tokenization
- Week 2 Wrap up
Lecture Notes (Optional)
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1
Readings
1
Readings
- Lecture Notes Week 2
Weekly Assignment - More on the BBC News Archive
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1
Programming
- Diving deeper into the BBC News archive
Sequence models
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10
Videos
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3
Readings
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6
Lab
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1
Assignment
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Assignment
- Week 3 Quiz
6
Labs
- Check out the code! (Lab 1)
- Check out the code! (Lab 2)
- Check out the code! (Lab 3)
- Check out the code! (Lab 4)
- Exploring a Bidirectional LSTM (Lab 5)
- Exploring a Convolutional Network (Lab 6)
10
Videos
- A conversation with Andrew Ng
- Introduction
- LSTMs
- Implementing LSTMs in code
- Accuracy and loss
- A word from Laurence
- Looking into the code
- Using a convolutional network
- Going back to the IMDB dataset
- Tips from Laurence
3
Readings
- Link to Andrew's sequence modeling course
- More info on LSTMs
- Week 3 Wrap up
Lecture Notes (Optional)
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1
Readings
1
Readings
- Lecture Notes Week 3
Weekly Assignment - Exploring overfitting in NLP
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Programming
- Exploring overfitting in NLP
Sequence models and literature
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13
Videos
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1
Readings
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3
Lab
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1
Assignment
1
Assignment
- Week 4 Quiz
3
Labs
- Check out the code! (Lab 1)
- Check out the code! (Lab 2)
- (optional) Generating text using a character-based RNN
13
Videos
- A conversation with Andrew Ng
- Introduction
- Looking into the code
- Preparing the training data
- More on the training data
- Finding what the next word should be
- Example
- Predicting a word
- Notebook for lesson 1
- Poetry!
- Looking into the code
- Laurence the poet!
- Your next task
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Readings
- Link to the dataset
Lecture Notes (Optional)
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1
Readings
1
Readings
- Lecture Notes Week 4
End of Access to Lab Notebooks
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Readings
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Readings
- [IMPORTANT] Reminder about end of access to Lab Notebooks
Weekly Assignment - Generate Shakespeare-like text
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Programming
- Predicting the next word
Course 3 Wrap up
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1
Videos
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Readings
1
Videos
- A conversation with Andrew Ng
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Readings
- Wrap up
Acknowledgments
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Readings
1
Readings
- Acknowledgments
Auto Summary
Discover how to build scalable AI-powered algorithms with the "Natural Language Processing in TensorFlow" course. Ideal for software developers, this course delves into text processing, tokenization, and neural network applications using TensorFlow. Led by Coursera, it includes training on RNNs, GRUs, and LSTMs, culminating in creating original poetry with LSTM. Lasting 1440 minutes, it offers flexible subscription options for a professional audience.
Laurence Moroney