**Introduction**

**Step 1: Defining the Hyperparameters to Tune**

**Step 2: Selecting an Optimization Method**

**Step 3: Performing the Hyperparameter Optimization**

**Example: Hyperparameter Optimization with PyTorch in Action**

```
import torch.optim as optim
from random_search import random_search
# Define the hyperparameter search space
search_space = {
'learning_rate': [1e-4, 1e-3, 1e-2],
'batch_size': [32, 64, 128]
}
# Define the model training and evaluation functions
def train_model(hyperparams):
# Train the model with the given hyperparameters
# ...
def evaluate_model(hyperparams):
# Evaluate the model performance on the validation set
# ...
# Perform random search for hyperparameter optimization
best_hyperparams = random_search(search_space, train_model, evaluate_model)
print("Best hyperparameters found: ", best_hyperparams)
```

**Conclusion**

Hyperparameter optimization is an essential step in training a machine learning model. By finding the best hyperparameters, you can improve the performance of your model and achieve better results. Keep learning and experimenting! 🚀