Long Short-Term Memory (LSTM)
LSTM (Long Short-Term Memory) networks are one of the most important inventions in deep learning.
They are a special type of Recurrent Neural Network (RNN) designed to capture long-term dependencies
and solve one of the biggest problems of standard RNNs — the vanishing gradient problem.
LSTMs power many real-life technologies: speech recognition, Google Translate, sentiment analysis,
chatbots, text generation, and financial forecasting. They are designed to remember information for
long periods and forget unnecessary details intelligently.
📌 Why Standard RNNs Fail
Standard RNNs work well for short sequences but fail when the sequence is long.
For example, consider this sentence:
“The movie that I watched yesterday at the new theater was absolutely wonderful.”
To understand “wonderful,” the model needs to remember the context from the beginning.
Standard RNNs forget earlier information because gradients shrink during backpropagation.
This is called: Vanishing Gradient Problem
LSTMs were created to solve this.
⭐ What Is an LSTM?
An LSTM is an advanced type of RNN that can remember information over long sequences.
It has a special internal structure called the cell state that allows long-term memory.
LSTM = RNN + Memory + Control Gates
📌 The Three Gates of LSTM
LSTMs use three “gates” to control information flow.
1. Forget Gate
Decides what information to forget from the previous memory.
forget_gate = sigmoid( Wf * [h(t-1), x(t)] )
Real-life example:
You forget unnecessary details from yesterday’s conversation.
2. Input Gate
Decides what new information to store in the memory.
input_gate = sigmoid( Wi * [h(t-1), x(t)] )
candidate = tanh( Wc * [h(t-1), x(t)] )
3. Output Gate
Decides what part of the memory should be output at this timestep.
output_gate = sigmoid( Wo * [h(t-1), x(t)] )
h_t = output_gate * tanh(cell_state)
📌 Cell State — The LSTM Memory Highway
The cell state is the secret power of LSTMs.
It allows information to flow unchanged for many timesteps.
This solves the vanishing gradient problem and helps LSTMs remember long sequences.
⭐ Real-Life Examples of LSTM
1. Machine Translation (Google Translate)
LSTMs understand the meaning of long sentences and translate them accurately.
2. Speech Recognition (Siri, Google Assistant)
LSTMs convert spoken words to text by processing audio sequences.
3. Text Generation (Story Writing, Chatbots)
LSTMs generate text that sounds natural and context-aware.
4. Sentiment Analysis
LSTMs detect emotion (positive/negative) in long reviews.
5. Time-Series Forecasting
Used for:
- Stock price forecasting
- Weather prediction
- Electricity usage patterns
- Sales forecasting
⭐ LSTM Architecture (Keras Example)
from tensorflow import keras
from keras import layers
model = keras.Sequential([
layers.LSTM(128, return_sequences=True),
layers.LSTM(64),
layers.Dense(1, activation='sigmoid')
])
This model can process long text sequences, speech signals, or time-series data.
📌 Bidirectional LSTM
Reads the sequence in both directions.
model = keras.Sequential([
layers.Bidirectional(layers.LSTM(64)),
layers.Dense(1, activation='sigmoid')
])
Useful for NLP tasks where context comes from both sides.
📌 GRU (Gated Recurrent Unit) — Simplified LSTM
GRU is a simpler version of LSTM with only 2 gates.
It trains faster and performs similarly.
📌 LSTM vs RNN
- RNN: Short-term memory, vanishing gradients
- LSTM: Long-term memory, stable training
📌 LSTM vs GRU
- LSTM: More accurate, but slower
- GRU: Faster, fewer parameters
📌 When to Use LSTM
- Text classification
- Machine translation
- Chatbots
- Speech recognition
- Time-series forecasting
- Music generation
- Sequence prediction
📌 Summary
LSTMs are powerful sequence models designed to remember information over long timesteps.
They fix the memory limitations of traditional RNNs and are widely used in NLP, speech,
forecasting, and AI applications. In the next chapter, we will explore
Autoencoders — a technique for data compression and generation.
