Building a Character-Level RNN Text Generator from Scratch: Shakespeare’s Digital Apprentice

Author(s): Souradip Pal

Originally published on Towards AI.

Ever dreamed of teaching a machine to write like Shakespeare? Imagine watching your own AI spin out sonnets, one letter at a time — it’s possible, and simpler than you think.

Today, we’re peeling back the curtain on neural text generation. Not the complex, industrial-sized models like ChatGPT. But a humble, handmade version: a character-level Recurrent Neural Network (RNN) trained to mimic the Bard himself.

Why should you care about character-level models? Because they do more with less. While most models focus on predicting whole words, this one works with individual characters. It’s lean, flexible, and surprisingly powerful.

You’re about to see how a neural network can learn to write like Shakespeare — from scratch. Not with fancy word libraries or pre-trained behemoths. Just a simple loop learning the rhythm of old English, letter by letter.

And here’s the twist: this tiny model doesn’t just copy. It invents. It learns patterns, spellings, punctuation — everything — just from the raw text.

Why Character-Level RNNs? They’re ideal for small datasets. They don’t rely on a massive vocabulary — just a few dozen characters. And they generate new, never-seen-before words, bringing a creative edge you won’t find in word-level models.

These networks learn language from its building blocks. They internalize grammar, rhythm, and structure — all without explicit rules. Perfect for tasks like stylized text generation, especially when your dataset is limited.

But we’re not stopping at just training… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI