Uploaded December 2025 | Updated September 2026, 2 weeks ago
Ever wonder what the bottleneck was in older LLM architectures like LSTMs?
They had to compress an entire sequence into a single fixed-size vector, leading to data loss and the model forgetting the start of the sentence.
Enter Attention in 2014! Don't throw away the intermediate states. Attention gives the decoder access to the entire input history, adjusted by relevance, to create a dynamic Context Vector.
This breakthrough led to a massive jump in translation quality for Google Translate in 2016.However, the sequential processing was still an O(n) operation - the next problem!
We need to go faster and use parallelization... We need the Transformer!
#attention #llm #transformers #deeplearning #machinelearning #aiexplained #lstm #development #ai #techshorts #neuralnetworks #artificialintelligence #jetbrains #python
Ever wonder what the bottleneck was in older LLM architectures like LSTMs?
They had to compress an entire sequence into a single fixed-size vector, leading to data loss and the model forgetting the start of the sentence.
Enter Attention in 2014! Don't throw away the intermediate states. Attention gives the decoder access to the entire input history, adjusted by relevance, to create a dynamic Context Vector.
This breakthrough led to a massive jump in translation quality for Google Translate in 2016.However, the sequential processing was still an O(n) operation - the next problem!
We need to go faster and use parallelization... We need the Transformer!
#attention #llm #transformers #deeplearning #machinelearning #aiexplained #lstm #development #ai #techshorts #neuralnetworks #artificialintelligence #jetbrains #python
