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+---
+Title: 'Adam'
+Description: 'Adam (Adaptive Moment Estimation) is an optimization algorithm designed to train neural networks efficiently by combining elements of AdaGrad and RMSProp.'
+Subjects:
+  - 'Computer Science'
+  - 'Machine Learning'
+Tags:
+  - 'PyTorch'
+  - 'Optimizers'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/data-science'
+---
+
+**Adam (Adaptive Moment Estimation)** is a popular optimization algorithm used to train [neural networks](https://www.codecademy.com/resources/docs/ai/neural-networks) in PyTorch. It incorporates the benefits of **AdaGrad** and [RMSProp](https://www.codecademy.com/resources/docs/pytorch/optimizers/rmsprop) algorithms, making it effective for handling sparse gradients and optimizing non-stationary objectives. Adam uses adaptive learning rates for each parameter and incorporates momentum to accelerate convergence.
+
+## Syntax
+
+In PyTorch, the `torch.optim.Adam` class is used to implement the Adam optimizer:
+
+```pseudo
+torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False, ...)
+```
+
+- `params`: The iterable of parameters to optimize (typically `model.parameters()`).
+- `lr` (Optional): The learning rate (default is `0.001`).
+- `betas` (Optional): The coefficients used for computing running averages of gradient and squared gradient (default is `(0.9, 0.999)`).
+- `eps` (Optional): The value added to the denominator to prevent division by zero (default is `1e-8`).
+- `weight_decay` (Optional): The L2 regularization factor (default is `0`).
+- `amsgrad` (Optional): If `True`, uses the AMSGrad variant of the Adam optimizer (default is `False`).
+
+> **Note:** The ellipsis (`...`) indicates that there can be additional optional parameters beyond those listed here, depending on specific use cases.
+
+## Example
+
+The following example demonstrates the usage of the Adam optimizer to train a neural network in PyTorch:
+
+```py
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# Define the model
+model = nn.Sequential(
+  nn.Linear(1, 1) # Single linear layer
+)
+
+# Define the loss function (MSELoss) and Adam optimizer
+criterion = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=0.01)
+
+# Sample training loop
+for epoch in range(50):
+  # Sample input and target
+  x = torch.randn(5, 1) # 5 samples, 1 feature each
+  y = 2*x + 1 # Linear relationship
+
+  # Forward pass
+  output = model(x)
+  loss = criterion(output, y)
+
+  # Backward pass and optimization
+  optimizer.zero_grad()
+  loss.backward()
+  optimizer.step()
+
+  # Print loss every 10 epochs
+  if epoch % 10 == 0:
+    print(f'Epoch [{epoch}/100], Loss: {loss.item():.4f}')
+```
+
+A sample output from the training loop might look like this (actual values may vary due to randomness):
+
+```shell
+Epoch [0/100], Loss: 0.4287
+Epoch [10/100], Loss: 9.2974
+Epoch [20/100], Loss: 3.8182
+Epoch [30/100], Loss: 1.9353
+Epoch [40/100], Loss: 14.0286
+```