Modern Natural Language Processing in Python
Solve Seq2Seq and Classification NLP tasks with Transformer and CNN using Tensorflow 2 in Google Colab
4.48 (1745 reviews)
49 506
students
6 hours
content
Jan 2025
last update
$84.99
regular price
What you will learn
Build a Transformer, new model created by Google, for any sequence to sequence task (e.g. a translator)
Build a CNN specialized in NLP for any classification task (e.g. sentimental analysis)
Write a custom training process for more advanced training methods in NLP
Create customs layers and models in TF 2.0 for specific NLP tasks
Use Google Colab and Tensorflow 2.0 for your AI implementations
Pick the best model for each NLP task
Understand how we get computers to give meaning to the human language
Create datasets for AI from those data
Clean text data
Understand why and how each of those models work
Understand everything about the attention mechanism, lying behind the newest and most powerful NLP algorithms
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Our Verdict
This course dives deep into modern NLP concepts with a strong focus on the Transformer model. While the content can be demanding for beginners, its hands-on approach utilizes Tensorflow 2.0 and offers an invaluable introduction to cutting-edge topics in NLP. Enhanced through example explanations and revisions of visually denser segments, it would prove even more engaging and accessible to learners across various skill levels.
What We Liked
- Comprehensive coverage of advanced NLP concepts such as Transformer, Positional Encoding, and Attention Mechanism
- Hands-on experience with implementing cutting-edge NLP models in Python using Tensorflow 2.0 on Google Colab
- Well-structured course focusing on recent research and developments in NLP, including the application of CNN for classification tasks
- Includes building a custom training process and creating custom layers and models for specific NLP tasks
Potential Drawbacks
- Slides and explanations can be denser and more visual to facilitate understanding of complex concepts
- Lacks comprehensive examples, particularly in the Transformer section, which could aid learners' comprehension
- Some users found the course pace challenging, especially for those new to NLP or lacking experience with RNN and LSTM
- Occasionally lacks in-depth discussions of implementation decisions compared to other resources
2518066
udemy ID
20/08/2019
course created date
21/11/2019
course indexed date
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