NLP - 11: Encoder-Decoder Model
NLP - 11: Encoder-Decoder Model
In this video, we introduce the Encoder-Decoder architecture, one of the most important deep learning frameworks for sequence-to-sequence tasks in NLP.
We explain how the encoder captures input information into a context representation, and how the decoder uses this context to generate meaningful output sequences. This forms the foundation of tasks such as machine translation, text summarization, and question answering.
What’s Covered:
• Motivation for sequence-to-sequence models
• Encoder-Decoder architecture explained step by step
• Role of the encoder in capturing input sequences
• Role of the decoder in generating output sequences
• How hidden states and context vectors work
• Applications of encoder-decoder models in NLP
This session is perfect for learners preparing to understand attention mechanisms and Transformers, as the encoder-decoder framework is the stepping stone toward modern architectures.
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#NLP #EncoderDecoder #Seq2Seq #DeepLearning #NeuralNetworks #TextSummarization #MachineTranslation #PythonNLP #SequenceModeling
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