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Time-series data and natural language have a hidden structure. Here is how we model it.

Modeling sequences is one of the most challenging tasks in AI, moving from static inputs to temporal flows. From Gated Recurrent Units (GRUs) to the revolutionary Multi-Head Attention mechanisms, the MIT 6.S191 sequence modeling lecture reveals the evolution of "recurrent memory."

Key technical takeaways:

RNNs: Solving sequential dependence with hidden states.
LSTMs/GRUs: Mitigating the vanishing gradient problem with memory gates.
Attention: The turning point—allowing the model to globally weight context beyond proximity.
Transformers: Parallelizable architectures that eliminated the sequential bottleneck.

From financial forecasting to LLMs, sequence modeling is the backbone of time-sensitive AI.

#LearnByDoingWithSteven #SequenceModeling #Transformers #RNNs #AttentionIsAllYouNeed #MIT #NaturalLanguageProcessing #DeepLearning #AI

All my links: https://linktr.ee/learnbydoingwithsteven