Autograd is a key component for implementing automatic differentiation. It allows us to compute gradients automatically for tensor operations, which is crucial for training neural networks efficiently using techniques like backpropagation. This tutorial will provide an overview of PyTorch Autograd, covering the following topics:

- Introduction to Autograd
- Autograd in model training
- Conclusion

Let's get started.

**Introduction to Autograd**

Autograd, short for automatic differentiation, is a core feature of PyTorch that enables automatic computation of gradients for tensor operations. Gradients are important in machine learning for optimization algorithms like gradient descent, which are used to update the parameters of neural network models during training.

Autograd is enabled by default in PyTorch. To compute gradients for a tensor, we need to set the requires_grad attribute of the tensor to True.

PyTorch's Autograd system works by dynamically building a computational graph during the forward pass of tensor operations. This graph records the operations applied to tensors and their dependencies, which is then used to compute gradients during the backward pass. The backward() method is used to compute gradients automatically. After calling backward(), the gradients are stored in the grad attribute of the input tensors.

The result looks as follows:

**Autograd in model training**

To show the usage of Autograd, let's see an example with neural network model training. Below code demonstrates the training of a simple neural network model using PyTorch, showing the role of the Autograd module and its importance in automatic differentiation.

- We define SimpleNN class with two linear layers (fc1 and fc2) and a sigmoid activation function. This model is capable of performing binary classification.
- Prepare training data X and corresponding labels y are defined. These are tensors containing input features and target labels, respectively.
- The Binary Cross Entropy (BCE) loss function is defined to measure the difference between model predictions and actual labels. Additionally, the Stochastic Gradient Descent (SGD) optimizer is instantiated to update the model parameters based on computed gradients.
- In training loop, we implement:

- Loss computation: The BCE loss is calculated based on the model predictions and actual labels.
- Preventing gradient accumulation: The optimizer.zero_grad() is a method call that zeroes out (or resets) the gradients of all parameters optimized by the optimizer. It ensures that gradients are fresh and correctly computed for each batch during the training process to optimize the parameters effectively.
- Backward pass and optimization: Gradients of the loss with respect to model parameters are computed using the backward() method. This is a crucial step enabled by autograd, as it automatically calculates these gradients, facilitating efficient training of the neural network. Then, the optimizer updates the model parameters using these gradients through the step() method.
- Forward pass: The input data X is passed through the model to obtain predictions (outputs).

5. Finally, we do prediction on the same data and check the accuracy.

The output:

**Conclusion**

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