Recurrent Neural Networks

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🔄 Why Recurrent Neural Networks?

Imagine trying to predict the next word in this sentence using a regular neural network. Without remembering what came before, it's impossible! The word "it" refers to something mentioned earlier. Time matters. Order matters. Context matters.

Recurrent Neural Networks (RNNs) are designed specifically for sequential data where:

• Order is critical: "Dog bites man" ≠ "Man bites dog"
• Length varies: Sentences can be 5 words or 50 words
• Context is needed: Understanding depends on what came before
• Patterns repeat: Same features detected at different positions in sequence

Sequential Data is Everywhere

Why Not Use Regular Neural Networks?

Real-World Example: Sentiment Analysis

🧠 Understanding Basic RNNs

RNN Architecture: Unfolding Through Time

Think of an RNN as the same neural network applied repeatedly at each time step. It's like having one worker process a queue of tasks, carrying context from task to task.

Time:    t=0          t=1          t=2          t=3
Input:   x₀    →      x₁    →      x₂    →      x₃
         ↓            ↓            ↓            ↓
Hidden:  h₀  ─────→  h₁  ─────→  h₂  ─────→  h₃
         ↓            ↓            ↓            ↓
Output:  y₀           y₁           y₂           y₃

Same RNN cell, different time steps!
Hidden state h passes information forward →

The Mathematics

At each time step t, the RNN performs these computations:

Step-by-Step Example: Sentiment Prediction

Parameter Sharing Across Time

Crucial insight: The same weight matrices (W_hh, W_xh, W_hy) are used at every time step! This means:

• ✅ Fixed number of parameters regardless of sequence length
• ✅ Learning at one position helps at all positions
• ✅ Can process sequences of any length
• ✅ Much more efficient than separate networks for each position

Types of RNN Architectures

The Critical Problem: Vanishing Gradients

Basic RNNs have a fundamental limitation when learning long sequences:

Example: Why Vanishing Gradients Are a Problem

Basic RNN Implementation

import numpy as np

class SimpleRNN:
    def __init__(self, input_size, hidden_size, output_size):
        # Initialize weights
        self.Whh = np.random.randn(hidden_size, hidden_size) * 0.01
        self.Wxh = np.random.randn(hidden_size, input_size) * 0.01
        self.Why = np.random.randn(output_size, hidden_size) * 0.01
        self.bh = np.zeros((hidden_size, 1))
        self.by = np.zeros((output_size, 1))
    
    def forward(self, inputs):
        """
        Forward pass through time
        inputs: list of input vectors [x1, x2, x3, ...]
        """
        h = np.zeros((self.Whh.shape[0], 1))  # Initial hidden state
        self.hidden_states = []
        outputs = []
        
        for x in inputs:
            # Update hidden state
            h = np.tanh(self.Whh @ h + self.Wxh @ x + self.bh)
            self.hidden_states.append(h)
            
            # Compute output
            y = self.Why @ h + self.by
            outputs.append(y)
        
        return outputs, self.hidden_states

# ============ USING TENSORFLOW/KERAS ============
import tensorflow as tf

# Simple RNN for sentiment analysis
model = tf.keras.Sequential([
    tf.keras.layers.SimpleRNN(64, input_shape=(None, embedding_dim)),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Note: SimpleRNN suffers from vanishing gradients!
# For real applications, use LSTM or GRU

🚀 LSTM: Long Short-Term Memory Networks

Long Short-Term Memory (LSTM) networks, introduced by Hochreiter & Schmidhuber in 1997, revolutionized sequence modeling by solving the vanishing gradient problem. The key innovation: a sophisticated gating mechanism that carefully controls information flow.

LSTM Architecture: The Cell State Highway

An LSTM cell has two types of state:

• Cell state (c_t): Long-term memory, runs straight through with minimal modifications — the "information highway"
• Hidden state (h_t): Short-term memory, filtered version of cell state for immediate use

The Four Components of LSTM

1. Forget Gate 🚪 (What to Discard)

Decides what information to throw away from the cell state. Looks at h_t-1 and x_t, outputs numbers between 0 (completely forget) and 1 (completely keep) for each number in cell state.

Example: In "The cat was cute. The dog..." → Forget gate might output 0.1 for "cat" features (forget) when seeing "dog" (new subject).

2. Input Gate 📝 (What New Information to Add)

Decides what new information to store in cell state. Two parts:

• Input gate: Which values to update
• Candidate values: What new values to add

Example: When seeing "dog", input gate might output 0.9 (strong add) for new subject features.

3. Cell State Update 🔄 (Combining Old + New)

Update cell state: (old state) × (forget gate) + (new candidates) × (input gate)

Critical: This is addition, not multiplication through tanh! Gradients can flow back unchanged → no vanishing!

4. Output Gate 📤 (What to Output)

Decides what parts of cell state to output as hidden state. Filtered version of cell state.

Example: Output gate might output 0.8 for subject-related features (relevant now) but 0.1 for setting-related features (stored but not immediately relevant).

Intuitive Example: Reading a Story

Why LSTMs Beat Vanishing Gradients

GRU: Gated Recurrent Unit (Simplified LSTM)

GRU (Cho et al., 2014) simplifies LSTM by merging cell state and hidden state, and combining forget & input gates. Often performs similarly with fewer parameters!

GRU Equations

LSTM vs GRU vs Basic RNN Comparison

Implementation: LSTM and GRU

import tensorflow as tf

# ============ LSTM EXAMPLE ============
lstm_model = tf.keras.Sequential([
    # Embedding: convert words to dense vectors
    tf.keras.layers.Embedding(vocab_size=10000, embedding_dim=128, input_length=100),
    
    # LSTM layer: 64 units
    # return_sequences=True: output at each time step (for stacking)
    # return_sequences=False: output only at last time step (for classification)
    tf.keras.layers.LSTM(64, return_sequences=True),
    
    # Second LSTM layer
    tf.keras.layers.LSTM(32),
    
    # Classification
    tf.keras.layers.Dense(1, activation='sigmoid')
])

lstm_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# ============ GRU EXAMPLE ============
gru_model = tf.keras.Sequential([
    tf.keras.layers.Embedding(10000, 128, input_length=100),
    
    # GRU: fewer parameters, often similar performance
    tf.keras.layers.GRU(64, return_sequences=True),
    tf.keras.layers.GRU(32),
    
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# ============ BIDIRECTIONAL RNN ============
# Process sequence forward AND backward (see future context too)
bidirectional_model = tf.keras.Sequential([
    tf.keras.layers.Embedding(10000, 128, input_length=100),
    
    # Bidirectional LSTM: 128 units total (64 forward + 64 backward)
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences=True)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),
    
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# ============ COMPARISON ============
print("LSTM parameters:", lstm_model.count_params())
print("GRU parameters:", gru_model.count_params())
print("Bidirectional parameters:", bidirectional_model.count_params())

💻 Building RNNs in Practice

Now let's build real RNN systems from scratch! We'll cover sentiment analysis, time series forecasting, and advanced techniques like bidirectional RNNs and stacking.

Example 1: Sentiment Analysis with IMDB Reviews

Let's build a complete pipeline to classify movie reviews as positive or negative using LSTM.

import tensorflow as tf
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np

# ============ 1. LOAD AND PREPARE DATA ============
# Load IMDB dataset (top 10,000 most common words)
vocab_size = 10000
max_length = 200  # Truncate reviews to 200 words

(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=vocab_size)

print(f"Training samples: {len(X_train)}")
print(f"Example review (encoded): {X_train[0][:10]}...")  # First 10 words
print(f"Label: {y_train[0]}")  # 0=negative, 1=positive

# Pad sequences to same length (RNNs need consistent input shape for batching)
X_train = pad_sequences(X_train, maxlen=max_length, padding='post', truncating='post')
X_test = pad_sequences(X_test, maxlen=max_length, padding='post', truncating='post')

# ============ 2. BUILD MODEL ============
model = tf.keras.Sequential([
    # Embedding: convert word IDs to dense vectors
    # Shape: (batch, max_length) -> (batch, max_length, embedding_dim)
    tf.keras.layers.Embedding(
        input_dim=vocab_size,      # Vocabulary size
        output_dim=128,             # Embedding dimension
        input_length=max_length     # Sequence length
    ),
    
    # Dropout for embedding regularization
    tf.keras.layers.Dropout(0.2),
    
    # LSTM layer 1: 64 units, return sequences for next LSTM
    # Shape: (batch, max_length, 128) -> (batch, max_length, 64)
    tf.keras.layers.LSTM(64, return_sequences=True, dropout=0.2),
    
    # LSTM layer 2: 32 units, return only final output
    # Shape: (batch, max_length, 64) -> (batch, 32)
    tf.keras.layers.LSTM(32, dropout=0.2),
    
    # Dense output layer
    tf.keras.layers.Dense(1, activation='sigmoid')  # Binary classification
])

model.compile(
    optimizer='adam',
    loss='binary_crossentropy',
    metrics=['accuracy']
)

print(model.summary())

# ============ 3. TRAIN MODEL ============
history = model.fit(
    X_train, y_train,
    epochs=5,
    batch_size=64,
    validation_split=0.2,
    verbose=1
)

# ============ 4. EVALUATE ============
test_loss, test_acc = model.evaluate(X_test, y_test)
print(f"\nTest Accuracy: {test_acc:.4f}")

# ============ 5. MAKE PREDICTIONS ============
# Test on a few examples
predictions = model.predict(X_test[:5])
for i in range(5):
    sentiment = "Positive" if predictions[i] > 0.5 else "Negative"
    true_sentiment = "Positive" if y_test[i] == 1 else "Negative"
    print(f"Review {i+1}: Predicted={sentiment} (confidence: {predictions[i][0]:.3f}), True={true_sentiment}")

Example 2: Time Series Forecasting with Stock Prices

Predict next day's stock price using past 60 days of data.

import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# ============ 1. PREPARE TIME SERIES DATA ============
# Assume we have stock price data
# dates: ['2023-01-01', '2023-01-02', ...]
# prices: [150.23, 152.45, ...]

# Normalize prices to [0, 1] range (neural nets work better with normalized data)
scaler = MinMaxScaler(feature_range=(0, 1))
prices_scaled = scaler.fit_transform(prices.reshape(-1, 1))

# Create sequences: use past 60 days to predict next day
def create_sequences(data, seq_length=60):
    X, y = [], []
    for i in range(len(data) - seq_length):
        X.append(data[i:i+seq_length])      # Past 60 days
        y.append(data[i+seq_length])        # Next day (target)
    return np.array(X), np.array(y)

seq_length = 60
X, y = create_sequences(prices_scaled, seq_length)

# Split: 80% train, 20% test
split = int(0.8 * len(X))
X_train, X_test = X[:split], X[split:]
y_train, y_test = y[:split], y[split:]

print(f"X_train shape: {X_train.shape}")  # (samples, 60, 1)
print(f"y_train shape: {y_train.shape}")  # (samples, 1)

# ============ 2. BUILD GRU MODEL ============
# GRU often works well for time series (simpler, faster than LSTM)
model = tf.keras.Sequential([
    # GRU layer 1: 50 units, return sequences
    tf.keras.layers.GRU(50, return_sequences=True, input_shape=(seq_length, 1)),
    tf.keras.layers.Dropout(0.2),
    
    # GRU layer 2: 50 units
    tf.keras.layers.GRU(50, return_sequences=False),
    tf.keras.layers.Dropout(0.2),
    
    # Output layer: predict single value
    tf.keras.layers.Dense(1)
])

model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae'])

# ============ 3. TRAIN ============
history = model.fit(
    X_train, y_train,
    epochs=50,
    batch_size=32,
    validation_split=0.1,
    verbose=1
)

# ============ 4. MAKE PREDICTIONS ============
predictions = model.predict(X_test)

# Denormalize back to actual prices
predictions = scaler.inverse_transform(predictions)
y_test_actual = scaler.inverse_transform(y_test)

# Calculate error
mae = np.mean(np.abs(predictions - y_test_actual))
print(f"Mean Absolute Error: ${mae:.2f}")

# Plot results (requires matplotlib)
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 6))
plt.plot(y_test_actual, label='Actual Price', color='blue')
plt.plot(predictions, label='Predicted Price', color='red', alpha=0.7)
plt.title('Stock Price Prediction')
plt.xlabel('Days')
plt.ylabel('Price ($)')
plt.legend()
plt.show()

Example 3: Bidirectional RNN for Text Classification

Process sequences in both forward and backward directions. Useful when future context helps (not for real-time prediction!).

import tensorflow as tf

# Bidirectional LSTM: process sequence forward AND backward
# "The movie was not very good" 
# Forward: accumulates "movie was not very good"
# Backward: sees "good" first, then "not" reverses meaning

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(10000, 128, input_length=200),
    
    # Bidirectional wrapper: runs LSTM forward and backward
    # Output size: 64*2 = 128 (concatenates forward + backward)
    tf.keras.layers.Bidirectional(
        tf.keras.layers.LSTM(64, return_sequences=True)
    ),
    
    # Another bidirectional layer
    tf.keras.layers.Bidirectional(
        tf.keras.layers.LSTM(32)
    ),
    
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

print(model.summary())
# Notice: Bidirectional doubles the parameters!

Example 4: Stacking RNN Layers

Multiple RNN layers can learn hierarchical representations: lower layers capture simple patterns, higher layers capture complex patterns.

# Deep stacked LSTM
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(10000, 128, input_length=200),
    
    # Layer 1: Learn low-level patterns (word combinations)
    # MUST use return_sequences=True to feed next LSTM
    tf.keras.layers.LSTM(128, return_sequences=True, dropout=0.2),
    
    # Layer 2: Learn mid-level patterns (phrase meanings)
    tf.keras.layers.LSTM(64, return_sequences=True, dropout=0.2),
    
    # Layer 3: Learn high-level patterns (sentence sentiment)
    # return_sequences=False: output only final hidden state
    tf.keras.layers.LSTM(32, dropout=0.2),
    
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# Rule of thumb: Each layer should have fewer units (funnel architecture)

Best Practices for Training RNNs

🚀 Real-World RNN Applications

RNNs power many applications you use daily! Let's explore real-world use cases with code examples.

1. Sentiment Analysis 💬

Classify text as positive, negative, or neutral. Used by companies to monitor brand reputation, customer feedback, and social media.

# Simple sentiment classifier
def predict_sentiment(text, model, tokenizer, max_length=200):
    # Tokenize and pad
    sequence = tokenizer.texts_to_sequences([text])
    padded = pad_sequences(sequence, maxlen=max_length)
    
    # Predict
    prediction = model.predict(padded)[0][0]
    
    if prediction > 0.6:
        return f"Positive (confidence: {prediction:.2f})"
    elif prediction < 0.4:
        return f"Negative (confidence: {1-prediction:.2f})"
    else:
        return f"Neutral (confidence: {0.5 + abs(0.5-prediction):.2f})"

# Example usage
text = "The movie started slow but ended amazingly!"
print(predict_sentiment(text, model, tokenizer))

2. Machine Translation 🌐

Translate text between languages using encoder-decoder architecture. RNNs pioneered neural machine translation before Transformers.

# Simplified encoder-decoder for translation
from tensorflow.keras.layers import Input, LSTM, Dense

# ============ ENCODER ============
encoder_inputs = Input(shape=(None, num_encoder_tokens))
encoder_lstm = LSTM(256, return_state=True)
encoder_outputs, state_h, state_c = encoder_lstm(encoder_inputs)
encoder_states = [state_h, state_c]  # Final hidden state = context vector

# ============ DECODER ============
decoder_inputs = Input(shape=(None, num_decoder_tokens))
decoder_lstm = LSTM(256, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

# Full model
model = tf.keras.Model([encoder_inputs, decoder_inputs], decoder_outputs)

3. Speech Recognition 🎤

Convert audio waveforms to text. Used in virtual assistants (Siri, Alexa), transcription services, voice commands.

# Speech recognition pipeline (simplified)
import librosa  # Audio processing library

# 1. Extract features from audio
audio, sr = librosa.load('speech.wav', sr=16000)
mfcc_features = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)

# 2. Build model
model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(None, 13)),  # Variable length sequences
    tf.keras.layers.Bidirectional(LSTM(128, return_sequences=True)),
    tf.keras.layers.Bidirectional(LSTM(128, return_sequences=True)),
    tf.keras.layers.Dense(29, activation='softmax')  # 26 letters + space + blank + apostrophe
])

# 3. Use CTC loss for alignment
model.compile(optimizer='adam', loss=tf.keras.backend.ctc_batch_cost)

4. Time Series Forecasting 📈

Predict future values based on historical patterns. Critical for business planning, energy management, financial trading.

5. Text Generation ✍️

Generate new text character-by-character or word-by-word. Applications: chatbots, content creation, code completion.

# Character-level text generation
import tensorflow as tf
import numpy as np

# 1. Prepare data: map characters to integers
text = "Your training text here..."
chars = sorted(set(text))
char_to_idx = {ch: i for i, ch in enumerate(chars)}
idx_to_char = {i: ch for i, ch in enumerate(chars)}

# Create sequences: "hell" -> "o", "ello" -> " ", etc.
seq_length = 40
X, y = [], []
for i in range(len(text) - seq_length):
    X.append([char_to_idx[ch] for ch in text[i:i+seq_length]])
    y.append(char_to_idx[text[i+seq_length]])

X = np.array(X)
y = tf.keras.utils.to_categorical(y, num_classes=len(chars))

# 2. Build model
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(len(chars), 128, input_length=seq_length),
    tf.keras.layers.LSTM(256, return_sequences=True),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.LSTM(256),
    tf.keras.layers.Dense(len(chars), activation='softmax')
])

model.compile(optimizer='adam', loss='categorical_crossentropy')

# 3. Generate text
def generate_text(model, start_string, length=100, temperature=1.0):
    input_seq = [char_to_idx[ch] for ch in start_string]
    generated = start_string
    
    for _ in range(length):
        # Predict next character
        x = np.array([input_seq])
        predictions = model.predict(x, verbose=0)[0]
        
        # Sample with temperature (higher = more random)
        predictions = np.log(predictions) / temperature
        exp_preds = np.exp(predictions)
        predictions = exp_preds / np.sum(exp_preds)
        
        next_idx = np.random.choice(len(chars), p=predictions)
        next_char = idx_to_char[next_idx]
        
        generated += next_char
        input_seq = input_seq[1:] + [next_idx]  # Slide window
    
    return generated

# Generate text starting with "The quick"
print(generate_text(model, "The quick", length=200, temperature=0.8))

6. Named Entity Recognition (NER) 🏷️

Identify and classify entities (people, organizations, locations) in text. Used for information extraction, question answering.

# NER with bidirectional LSTM-CRF
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, 128, input_length=max_length),
    
    # Bidirectional: see full context (past + future)
    tf.keras.layers.Bidirectional(LSTM(64, return_sequences=True)),
    
    # TimeDistributed: apply Dense to each time step
    tf.keras.layers.TimeDistributed(
        tf.keras.layers.Dense(num_tags, activation='softmax')
    )
])

# Each word gets a tag: B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC, O

RNN Applications Summary

📋 Summary & Key Takeaways

Congratulations! You've mastered Recurrent Neural Networks, the architecture that powers sequential data processing. Let's consolidate what you've learned.

Core Concepts

Mental Models

Practical Decision Guide

Common Pitfalls & Solutions

RNN Design Checklist

Practice Projects

Solidify your understanding with these hands-on projects:

What You've Mastered

What's Next?

You've conquered RNNs, but the story doesn't end here! In the next tutorial, Attention & Transformers, you'll learn the revolutionary architecture that:

• ✨ Processes sequences in parallel (not sequentially like RNNs)
• ✨ Handles 1000+ token dependencies effortlessly
• ✨ Powers GPT, BERT, and modern LLMs
• ✨ Revolutionized NLP and beyond (vision, multimodal AI)