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

In the first example, we are going to discuss the Stochastic Gradient Descent (SGD) optimization algorithm in deep learning using Python.

# Import necessary libraries
import numpy as np
from sklearn.datasets import make_classification
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Create a synthetic dataset
X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize features by removing the mean and scaling to unit variance
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Initialize the SGDClassifier with appropriate parameters
sgd_clf = SGDClassifier(loss='log', max_iter=1000, learning_rate='optimal')

# Fit the model on the training data
sgd_clf.fit(X_train, y_train)

# Evaluate the model on the testing data
accuracy = sgd_clf.score(X_test, y_test)
print("Accuracy: {:.2f}".format(accuracy))

In the second example, we are going to discuss the Adam optimization algorithm in deep learning using Python.

# Import necessary libraries
import numpy as np
from sklearn.datasets import make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Create a synthetic dataset
X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize features by removing the mean and scaling to unit variance
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Initialize the MLPClassifier with Adam optimizer
mlp_clf = MLPClassifier(solver='adam', max_iter=1000, random_state=42)

# Fit the model on the training data
mlp_clf.fit(X_train, y_train)

# Evaluate the model on the testing data
accuracy = mlp_clf.score(X_test, y_test)
print("Accuracy: {:.2f}".format(accuracy))

These examples demonstrate how to use Stochastic Gradient Descent and Adam optimization algorithms in deep learning using Python. These algorithms help in optimizing the parameters of the model to improve its performance during training.