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In this video, we're going to explore Transformers, a groundbreaking architecture that has transformed the field of Natural Language Processing (NLP). We'll break down how Transformers work, focusing on their key components such as self-attention, multi-head attention, and the roles of the encoder and decoder. Unlike traditional models, Transformers can handle long sequences and complex dependencies more efficiently, making them the backbone of advanced models like BERT, GPT, and others.
-- Frequently Asked Questions
✅ Question 1 What is the key difference between Transformers and traditional RNN models?
Answer:Unlike RNNs, which process data sequentially and can struggle with long-range dependencies, Transformers use self-attention to process the entire input sequence at once.
✅ Question 2 What is self-attention in a Transformer, and why is it important?
Answer:Self-attention is the mechanism that allows a Transformer to focus on different parts of the input sequence when generating an output. It calculates the relationships between words in a sequence, helping the model understand context and meaning more effectively. This is crucial for tasks like translation or summarization, where context matters.
✅ Question 3 What are encoders and decoders in the Transformer architecture?
Answer: The encoder processes the input sequence and generates a representation that summarizes the important information. The decoder then takes this representation and uses it to generate the output sequence, one word at a time, by focusing on both the encoder’s output and the previously generated output words. Together, they enable tasks like language translation or text generation.
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This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.
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