Select a path tailored to your role and goals. Each path covers the essential concepts while respecting your time constraints.
From fundamentals to advanced implementations, explore the complete journey of understanding RNNs.
Why This Matters in 2024+
Historical context, connections to modern LLMs, and stakeholder communication.
The Limitations of Vanilla Neural Networks
Variable-length sequences, 5 architecture types, and Turing completeness.
Building Memory into Networks
Core equations, hidden state updates, and the "optimization over programs" insight.
The Problem and Its Solution
Gradient multiplication, LSTM cell state, forget/input/output gates.
Next-Character Prediction
One-hot encoding, cross-entropy loss, temperature sampling.
What Can RNNs Learn?
Shakespeare, Wikipedia, LaTeX, Linux kernel - and neuron visualization.
Vision, Speech, and Translation
CNN+RNN for captioning, encoder-decoder, and multimodal applications.
The Most Important Innovation
Soft vs hard attention, Neural Turing Machines, bridge to Transformers.
When to Use (and Not Use) RNNs
RNN limitations, Transformer revolution, and build vs buy decisions.
From NumPy to PyTorch to Hugging Face
Three implementation tracks with progressive complexity.
Train Your Own Model
Three difficulty levels with gamified milestones and achievements.