🔑Transformers use word embedding to convert words into numerical values.
🗺️Positional encoding helps Transformers keep track of word order in sentences.
🔁Self-attention enables Transformers to correctly associate words and understand relationships.
🧠Back propagation is used to optimize the weights in Transformers during training.
💡Transformers are widely used in natural language processing tasks.
What is the purpose of word embedding in Transformers?
—Word embedding converts words into numerical values to be processed by neural networks.
How does positional encoding contribute to Transformers?
—Positional encoding allows Transformers to keep track of word order in sentences.
What is the role of self-attention in Transformers?
—Self-attention enables Transformers to correctly associate words and understand relationships.
How are the weights in Transformers optimized?
—The weights in Transformers are optimized using a technique called back propagation during training.
What are some common applications of Transformers?
—Transformers are widely used in various natural language processing tasks, such as machine translation and text generation.
00:00Introduction to Transformers and their relevance in natural language processing.
06:00Explanation of word embedding and its role in converting words into numerical values for neural networks.
12:00How positional encoding helps Transformers keep track of word order in sentences.
19:00Understanding self-attention and its importance in correctly associating words and understanding relationships.
26:00Explanation of back propagation and its role in optimizing the weights in Transformers during training.
32:00Applications of Transformers in various natural language processing tasks.
