What are the different types of optimization algorithms used in deep learning?

In the first example, we are going to use the Stochastic Gradient Descent (SGD) optimization algorithm in Python for deep learning. Step 1: Import the necessary libraries 
import numpy as np
import tensorflow as tf

Step 2: Define the model and compile it using SGD optimizer 
model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Step 3: Train the model on your dataset 
model.fit(x_train, y_train, epochs=10, batch_size=32)

In the second example, we are going to use the Adam optimization algorithm in Python for deep learning. Step 1: Import the necessary libraries 
import numpy as np
import tensorflow as tf

Step 2: Define the model and compile it using Adam optimizer 
model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

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

Step 3: Train the model on your dataset 
model.fit(x_train, y_train, epochs=10, batch_size=32)

These are just a few examples of optimization algorithms used in deep learning. By experimenting with different optimizers, you can find the one that works best for your specific model and dataset.

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