Applied Deep Learning – Class 30 | GRU Explained
Автор: gened
Загружено: 2026-02-11
Просмотров: 2
Описание:
In this session of Applied Deep Learning, we cover the theoretical foundations of GRU (Gated Recurrent Unit) in detail.
This lecture focuses purely on concept building — no coding — to help you deeply understand how GRU works internally before implementation.
📚 In this class, we discuss:
🔹 Why GRU was proposed as a simpler alternative
🔹 Internal architecture of GRU
🔹 Update Gate and Reset Gate explained intuitively
🔹 Comparison: LSTM vs GRU
🔹 When to use GRU in real-world problems
GRU is computationally efficient and performs very well on sequential data tasks such as:
✔ Text classification
✔ Sentiment analysis
✔ Time-series prediction
✔ NLP applications
This session is designed for students who want strong conceptual clarity in Deep Learning and Sequence Models.
If you’re learning NLP, preparing for interviews, or building strong DL fundamentals, this lecture will help you understand GRU from scratch.
📂 Notebook Link:
https://github.com/GenEd-Tech/Applied...
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#DeepLearning #GRU #RNN #LSTM #AI #MachineLearning #NLP #AppliedDeepLearning #DLTheory
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