How do different optimizers compare in Python machine learning?

What are the main differences between Adam and SGD optimizers in Python?

Adam is adaptive and adjusts learning rates for each parameter, while SGD uses a fixed learning rate and can be more stable.

Which optimizer is generally better for deep learning tasks?

Adam is often preferred for deep learning due to its adaptive nature, but SGD can perform better with proper tuning.

Can you provide a code example comparing these optimizers?

Sure! Here's a simple example using TensorFlow:

import tensorflow as tf

model = tf.keras.Sequential([...])

# Using Adam
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')

# Using SGD
model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy')

What factors should I consider when choosing an optimizer?

Consider the model architecture, dataset size, convergence speed, and whether you need adaptive learning rates.