This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
Overview
Syllabus
- Encoder-Decoder Architecture: Overview
- Encoder-Decoder Architecture: Overview
- Encoder-Decoder Architecture: Lab Walkthrough
- Encoder-Decoder Architecture: Quiz
- Encoder-Decoder Architecture: Lab Resources
- Your Next Steps
- Course Badge
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The Encoder-Decoder Architecture course by Google on Google Cloud Skills Boost offers a comprehensive introduction to deep learning models used in natural language processing (NLP) and machine translation. It effectively covers the core concepts of the encoder-decoder structure, which transforms input data into fixed-size representations (encoders) and then decodes them into outputs. The course provides hands-on exercises that help solidify the understanding of building such models using TensorFlow, making it ideal for learners looking to enhance their AI and machine learning skills. The materials are well-structured, and the practical approach is highly beneficial for beginners and intermediates alike.