Natural Language Processing with Attention Models
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Course overview
- Provider
- Coursera
- Course type
- Free online course
- Level
- Intermediate
- Deadline
- Flexible
- Duration
- 37 hours
- Certificate
- Paid Certificate Available
- Course author
- Younes Bensouda Mourri
-
Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.
Description
In Course 4 of the Natural Language Processing Specialization, you will:a) Translate complete English sentences into German using an encoder-decoder attention model,
b) Build a Transformer model to summarize text,
c) Use T5 and BERT models to perform question-answering, and
d) Build a chatbot using a Reformer model.
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!
Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework (e.g., TensorFlow, Keras), as well as proficiency in calculus, linear algebra, and statistics. Please make sure that you’ve completed course 3 - Natural Language Processing with Sequence Models - before starting this course.
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. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
