Title: Understanding LSTM Networks
Authors: Sepp Hochreiter & Jürgen Schmidhuber
Published: 1997
Summary
Before Transformers took over the world, Recurrent Neural Networks (RNNs) were all the rage. But standard RNNs had a big memory problem: they forgot long-range dependencies—aka they couldn’t remember what you said five seconds ago. That’s where Long Short-Term Memory (LSTM) networks came in.
This 1997 paper introduced a new architecture with special units (called “memory cells”) that can store information over long time periods. The secret? Gates. Specifically, input, output, and forget gates that decide what to keep, update, or discard. It sounds simple now, but at the time, it was revolutionary.
🦴 Why It Still Matters
📱 Powers voice assistants, text prediction, and time-series forecasting
🧠 Solves the “vanishing gradient” problem plaguing older RNNs
🪄 A stepping stone to modern architectures like GRUs and Transformers
🔗 Read the Original Paper
Understanding LSTM Networks – Hochreiter & Schmidhuber, 1997 (PDF)
Essential Vocabulary
LSTM (Long Short-Term Memory): A type of recurrent neural network (RNN) designed to remember important information over long sequences—and forget what it doesn’t need. Think of it as: 🧠 Memory + Filters = Smarter learning over time.
RNN (Recurrent Neural Network): A neural network where connections loop back on themselves to handle sequential data.
Vanishing Gradient Problem: A training issue where gradients shrink too much during backpropagation, making it hard for the model to learn long-term dependencies.
Memory Cell: A structure in LSTM that preserves important information over time.
Gates (Input/Forget/Output): Mechanisms that decide what information to keep, discard, or pass forward in an LSTM.
🎁 Bonus: A Visual Companion
Chris Olah’s blog post is one of the clearest explanations of how LSTMs work—with diagrams, animations, and intuition:
👉 Understanding LSTM Networks – Blog Post (2015)
🗣 Let’s Keep Reading
Day 3 — RNNs gone rogue: Karpathy’s blog post that made machines write like Shakespeare.
#TheWolfReadsAI #LSTM #DeepLearning #AIExplained #NeuralNetworks #MLPapers #MachineLearning #RNN #AIHistory #SeppHochreiter #JurgenSchmidhuber #ChrisOlah #LTSMNetworks #DeepLearningwiththeWolf
Share this post