Gradient descent is the most widely used optimization algorithm in deep learning. It helps neural networks learn by minimizing the loss function. However, there are several variants of gradient descent, each with its advantages and use cases. Understanding these variants is crucial for efficient model training and faster convergence.<\/p>\n\n\n\n
Why Gradient Descent Variants Matter<\/strong><\/p>\n\n\n\n 1. Batch Gradient Descent<\/strong><\/p>\n\n\n\n 2. Stochastic Gradient Descent (SGD)<\/strong><\/p>\n\n\n\n 3. Mini-Batch Gradient Descent<\/strong><\/p>\n\n\n\n 4. Momentum-Based Gradient Descent<\/strong><\/p>\n\n\n\n 5. Nesterov Accelerated Gradient (NAG)<\/strong><\/p>\n\n\n\n 6. Adaptive Gradient Methods<\/strong><\/p>\n\n\n\n Choosing the Right Variant<\/strong><\/p>\n\n\n\n Example: Mini-Batch Gradient Descent in Python<\/strong><\/p>\n\n\n\n Best Practices<\/strong><\/p>\n\n\n\n Applications<\/strong><\/p>\n\n\n\n Lesson Summary<\/strong>\n
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w = w – learning_rate * v<\/li>\n<\/ul>\n\n\n\n\n
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import numpy as np# Dummy data
X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])w = 0.0
learning_rate = 0.01
batch_size = 2for epoch in range(50):
for i in range(0, len(X), batch_size):
X_batch = X[i:i+batch_size]
y_batch = y[i:i+batch_size]
# Forward pass
y_pred = X_batch * w
# Compute gradient
grad = np.mean(2 * (y_pred - y_batch) * X_batch)
# Update weight
w -= learning_rate * gradprint(\"Trained weight:\", w)<\/pre>\n\n\n\n\n
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Gradient descent variants are essential for efficiently training deep learning models. From batch and stochastic approaches to momentum and adaptive methods like Adam, choosing the right variant ensures faster convergence, better stability, and improved model performance. Understanding these techniques is crucial for intermediate and advanced deep learning projects.<\/p>\n\n\n