Transformer | Part 1: Encoder | Transformer Architecture Clearly Explained | Deep Learning

Описание: If you’ve ever struggled to truly understand Transformers, this video will change that.

In this deep dive, we break down the Transformer's Encoder part from the ground up with strong intuition so you can understand why every component exists instead of just memorizing the architecture.
Before jumping into the encoder, we first answer a critical question:

Why do we even need Transformers when we already had Encoder-Decoder models with Attention?

Then we carefully explore the Transformer Encoder architecture step-by-step, explaining the reasoning behind each component.

By the end of this video you’ll understand the core intuition behind Transformers — the architecture powering modern AI systems like ChatGPT, GPT models, BERT, and large language models.

🚀 What You'll Learn in This Video:
✔ Why older encoder-decoder architectures with attention mechanism are not enough, where do they struggle?
✔ How transformer solved limitations of older encoder-decoder architectures with attention mechanism?
✔ The complete Transformer Encoder architecture explained step-by-step
✔ Why Feed Forward Neural Networks exist
✔ Why residual connections (adding inputs again) are necessary
Everything explained in detailed and intuitive manner.

📌 Rough Timeline:
0:00 : Intro + Why transformers are needed
04:36 : Transformer's Encoder part deep dive (Intuition + Maths)

🎯 Who This Video Is For:
Machine Learning beginners, Deep Learning students, AI researchers starting NLP, Anyone preparing for ML interviews or People who want an intuitive understanding of Transformers

📌 Coming Next:
In the upcoming video we’ll break down:
➡ Masked self attention and Cross-Attention
➡ Transformer Decoder architecture

So make sure to subscribe and turn on notifications 🔔

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