Natural Language Processing with Sequence Models
Master NLP techniques using RNNs, LSTMs, GRUs & Siamese networks for sentiment analysis and named entity recognition.
Master NLP techniques using RNNs, LSTMs, GRUs & Siamese networks for sentiment analysis and named entity recognition.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Natural Language Processing Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.5
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English
vپښتو, বাংলা, اردو, 4 more
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What you'll learn
Train neural networks for sentiment analysis using GLoVe embeddings
Generate synthetic text using Gated Recurrent Units (GRU)
Implement named entity recognition using LSTM networks
Develop Siamese networks for question similarity analysis
Build practical NLP applications with deep learning
Skills you'll gain
This course includes:
1.7 Hours PreRecorded video
3 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This course focuses on advanced natural language processing techniques using sequence models. Students learn to implement sentiment analysis using neural networks with GLoVe word embeddings, generate synthetic text using GRUs, perform named entity recognition with LSTMs, and develop Siamese networks for identifying similar questions. The course combines theoretical understanding with practical implementation through hands-on programming assignments and real-world applications.
Recurrent Neural Networks for Language Modeling
Module 1 · 10 Hours to complete
LSTMs and Named Entity Recognition
Module 2 · 4 Hours to complete
Siamese Networks
Module 3 · 6 Hours to complete
Fee Structure
Instructors
Stanford AI Educator Pioneers Global Learning Through Course Innovation and EdTech Leadership
Younes Bensouda Mourri is a distinguished AI educator and entrepreneur who has significantly impacted global tech education. Born and raised in Morocco, he earned his B.S. in Applied Mathematics and Computer Science and M.S. in Statistics from Stanford University, where he now teaches Artificial Intelligence both on campus and online. As the founder of LiveTech.AI, he develops AI tools to transform academic institutions, while his courses have reached over 1.3 million learners worldwide, with 23% securing AI-related jobs after completion. His contributions include co-creating Stanford's Applied Machine Learning, Deep Learning, and Teaching AI courses, as well as developing the highly successful Natural Language Processing Specialization for DeepLearning.AI. Starting as a teaching assistant in Andrew Ng's Machine Learning course, he rose to become an Adjunct Lecturer at Stanford by age 22, demonstrating his commitment to democratizing AI education. Through his work with major companies like ASML, CISCO, and Boston Consulting Group, he continues to advance AI education while focusing on developing innovative NLP tools for personalized feedback and chain-of-thought reasoning
Leading Innovator in AI and Deep Learning Education
Łukasz Kaiser is a prominent instructor at DeepLearning.AI, recognized for his significant contributions to the field of artificial intelligence. As a co-author of TensorFlow, the Tensor2Tensor and Trax libraries, and the influential Transformer paper, he has played a pivotal role in shaping modern AI methodologies. Currently serving as a Staff Research Scientist at Google Brain, Łukasz's research has profoundly impacted the AI community, particularly in the realm of natural language processing (NLP). His courses on Coursera, including "Natural Language Processing with Attention Models" and "Natural Language Processing with Sequence Models," provide learners with essential skills to navigate and implement advanced AI techniques.With a strong academic background and extensive experience in machine learning, Łukasz is dedicated to making complex concepts accessible to students worldwide. He teaches multiple courses in various languages, including French, Russian, Arabic, and Korean, demonstrating his commitment to global education. His work not only enhances the understanding of deep learning but also empowers aspiring data scientists and AI practitioners to develop innovative solutions in their respective fields. Through his engaging teaching style and expert knowledge, Łukasz Kaiser continues to inspire the next generation of AI professionals.
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4.5 course rating
1,136 ratings
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