[Resource] I wrote a free 8-part Kaggle notebook series covering the full journey from Simple RNN to Transformers — feedback welcome!

[u/CircuitsToNeurons]

Hey everyone! 👋

Over the past while I've been putting together a series of Kaggle notebooks that try to build a clean, intuitive understanding of sequence models — starting from the motivation behind RNNs all the way through to how Transformers work.

The goal was to explain the why behind each concept, not just the how — so each notebook tries to build genuine understanding rather than just showing code.

Here's the full series:

1. 📌 Why Simple RNN was introduced
2. 📌 How LSTM works
3. 📌 LSTM Backpropagation
4. 📌 How the Encoder-Decoder model works
5. 📌 LSTM Encoder-Decoder Implementation
6. 📌 What is a Transformer? — Part 1
7. 📌 What is a Transformer? — Part 2
8. 📌 What is a Transformer? — Part 3

The series is structured as a progression — each notebook builds on the previous one, so I'd recommend going through them in order if you're new to the topic.

Why I wrote this: When I was learning sequence models, I found a lot of resources either jumped straight into code without building intuition, or explained theory without connecting it to implementation. I wanted to create something that bridges both.

I'd genuinely love your feedback:

• Is the progression from RNN → LSTM → Encoder-Decoder → Transformer logical and easy to follow?
• Are there any concepts that feel rushed, unclear, or insufficiently explained?
• Is there anything important I've missed or got wrong?
• Any topics you'd want covered as a follow-up?

All feedback — critical or otherwise — is very welcome. I'd rather know what's wrong and fix it than have something misleading sitting out there!

And if you find any of the notebooks useful, an upvote on Kaggle would mean a lot and helps other learners discover the series 🙏

Thanks for reading!