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Encoder-Decoder Architecture: Sequence-to-Sequence Learning
Master encoder-decoder models for NLP tasks. Learn architecture, implementation, and training using TensorFlow and Keras.
Course Cost
₹ 2,699
Advanced
Skill Level
1 Hour
Self-paced lessons
This advanced course provides a comprehensive overview of the encoder-decoder architecture, a powerful machine learning model for sequence-to-sequence tasks. Students will learn about the main components of this architecture, its applications in natural language processing tasks such as machine translation and text summarization, and how to train and deploy these models. The course includes a hands-on lab where learners implement an encoder-decoder model for poetry generation using TensorFlow and Keras.
3,204 Enrolled
English
What you'll learn
Understand the main components and functioning of the encoder-decoder architecture
Learn how to apply encoder-decoder models to sequence-to-sequence tasks like machine translation and text summarization
Gain practical experience in training and generating text using encoder-decoder models
Develop skills in implementing encoder-decoder architectures using TensorFlow and Keras
Understand the role of attention mechanisms in improving model performance
Learn to write and customize encoder-decoder models for specific NLP tasks
Gain insights into the challenges and best practices in training sequence-to-sequence models
Develop a practical understanding through a hands-on lab on poetry generation
Skills you'll gain
This course includes:
28 Minutes PreRecorded video
1 quiz
Access on Mobile, Tablet, Desktop
FullTime access
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There is 1 module in this course
This course offers an in-depth exploration of the encoder-decoder architecture, a fundamental concept in modern natural language processing (NLP) and machine learning. The curriculum focuses on understanding and implementing this powerful architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. Students will learn about the main components of the encoder-decoder model, including attention mechanisms, and how these elements work together to process and generate sequential data. The course combines theoretical knowledge with practical application, featuring a hands-on lab where learners implement a poetry generation model using TensorFlow and Keras. By the end of the course, participants will have a solid understanding of how to build, train, and deploy encoder-decoder models for various NLP tasks.
Encoder-Decoder Architecture: Overview
Module 1 · 1 Hours to complete
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