L-8 | Transformer Encoder: Multi-Head Attention to FFN (Full Math)

In this video, we explain the Transformer Encoder in a clear and intuitive way, starting from the basics and building up step by step. You’ll learn: What happens inside a Transformer encoder layer How self-attention works conceptually What multi-head attention means Why the encoder input and output have the same shape How the feed-forward network (FFN) fits into the encoder How encoder layers are stacked and how information flows through them This video focuses on understanding, not memorization. We connect the math with intuition so you can clearly see how each part of the encoder contributes to learning better representations of tokens. Whether you’re a student, a beginner in deep learning, or someone revisiting Transformers, this explanation will help you build a solid foundation. 👍 If you find this helpful, like and share the video 💬 Let me know in the comments what topic you want next 📸 Follow me on Instagram: @codewithaarohihindi 🔗 https://instagram.com/codewithaarohihindi 📧 You can also reach me at: aarohisingla1987@gmail.com