Optimizers – SGD, Adam, RMSprop

Optimizers are algorithms used to adjust the weights of a neural network to minimize the loss function. They play a critical role in training deep learning models by guiding the learning process.

• SGD (Stochastic Gradient Descent): Updates weights using a single sample or mini-batch. It's simple and efficient but may struggle with noisy gradients and local minima.
• Adam (Adaptive Moment Estimation): Combines the advantages of AdaGrad and RMSprop by using momentum and adaptive learning rates. It's widely used for its robustness and speed.
• RMSprop: Adapts the learning rate for each parameter by dividing by a moving average of recent gradient magnitudes. Good for recurrent networks and non-stationary objectives.

Here's how to use different optimizers in TensorFlow/Keras and PyTorch:

TensorFlow/Keras

from tensorflow.keras.optimizers import SGD, Adam, RMSprop

# SGD
model.compile(optimizer=SGD(learning_rate=0.01), loss='mse')

# Adam
model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')

# RMSprop
model.compile(optimizer=RMSprop(learning_rate=0.001), loss='mse')

PyTorch

import torch.optim as optim

# SGD
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Adam
optimizer = optim.Adam(model.parameters(), lr=0.001)

# RMSprop
optimizer = optim.RMSprop(model.parameters(), lr=0.001)