RNN — Next Character Prediction

What I Did

Built an RNN that predicts the next character in a sequence.

Approach: Use SimpleRNN layer to process character sequences and learn patterns.

Example:

• Input: “hell”
• Output: “o”

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How It Works

Step 1: Tokenize characters

• Convert characters to integers
• Build vocabulary mapping

Step 2: Create sequences

• Slide window over text
• Each sequence predicts next character

Step 3: Train RNN

• SimpleRNN processes each character
• Hidden state carries memory
• Dense layer predicts next character

Step 4: Generate text

• Feed predictions back as input
• Generate character by character

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Key Concept: Hidden State (Memory)

RNN’s power: hidden state carries memory from previous steps

Input: "h" → hidden state = [memory of "h"]
Input: "e" → hidden state = [memory of "he"]
Input: "l" → hidden state = [memory of "hel"]
Input: "l" → hidden state = [memory of "hell"]

Each step remembers previous context.

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Architecture

• Embedding: Convert character indices to vectors
• SimpleRNN: Process sequences with memory
• Dense: Predict next character probability

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Main Learning Points

✅ Sequential data has order

✅ Hidden state = neural memory

✅ Time steps = processing one element at a time

✅ Vanishing gradient problem exists (even with RNN)

✅ LSTM/GRU are better for longer sequences

✅ Text generation = predict one character, feed back

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What Happens Inside

Each time step:

new_hidden = activation(input_weights × current_char + hidden_weights × previous_hidden)

This recurrence is why it’s called Recurrent Neural Network.

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Limitations

• SimpleRNN struggles with long sequences (vanishing gradient)
• Can only see past, not future
• Slow compared to Transformers

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GitHub

🔗 RNN-Project-Next-Character-Prediction

See the repository for full implementation.

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RNNs have memory. That’s what makes them powerful for sequences. 🚀