Encoder, Decoder or Both : Sequence-to-Sequence (Seq2Seq) - Machine Learning

Here's a breakdown of why encoder-decoder models are used, along with explanations of each component:

Encoder

• Purpose: Takes the input sequence (e.g., a sentence in English) and compresses it into a fixed-length vector called a "context vector" or "thought vector". This vector is meant to capture the essential meaning of the input.
• Analogy: Think of it as reading a book and summarizing the entire plot in a single sentence.

Decoder

• Purpose: Takes the context vector produced by the encoder and generates the output sequence (e.g., the translation of the sentence into French). It does this step-by-step, using the context vector and previously generated words to predict the next word in the sequence.
• Analogy: Imagine someone giving you that one-sentence summary of the book and asking you to retell the entire story in detail.

Why Both Are Needed

The encoder-decoder architecture is powerful because it allows for:

• Handling variable-length sequences: The input and output sequences can be of different lengths, which is crucial for tasks like machine translation where sentences in different languages rarely have a one-to-one word correspondence.
• Capturing long-range dependencies: The context vector allows the decoder to access information from anywhere in the input sequence, even if the relevant words are far apart. This is important for understanding complex sentence structures.
• Learning complex relationships: The model can learn intricate relationships between the input and output sequences, going beyond simple word-to-word mappings.

In short: The encoder understands the input, and the decoder uses that understanding to generate the output. They work together to handle complex sequence-to-sequence tasks.

#Transformer uses the Encoder and Decoder and following models are used in the market either Encoder, Decoder or both types. Please refer below image -

#LLM #LLMs #RAG #DeepSeek #DeepSeekR1 #DeepSeekAI #DataScience #DataProtection #dataengineering #data #Cloud #AWS #azuretime #Azure #AIAgent #MachineLearning #DeepLearning #langchain #AutoGen #PEOPLE #fyp #trending #viral #fashion #food #travel #GenerativeAI #ArtificialIntelligence #AI #AIResearch #AIEthics #AIInnovation #GPT4 #BardAI #Llama2 #AIArt #AIGeneratedContent #AIWriting #AIChatbot #AIAssistant #FutureOfAI #Gemini #Gemini_Art #ChatGPT #openaigpt #OpenAI #Microsoft #Apple #Meta #Netflix #Google #Alphabet