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Deep Learning With PyTorch - Full Course

What You Will Learn

Install PyTorch and set up a development environment
Create and manipulate tensors in PyTorch
Understand the basics of the Autograd package and backpropagation in PyTorch

Key Concepts

PyTorch is a popular deep learning framework that provides a dynamic computation graph and automatic differentiation. Tensors are the fundamental data structure in PyTorch, similar to NumPy arrays. The Autograd package in PyTorch provides a way to compute gradients automatically, which is essential for training neural networks. Backpropagation is an algorithm used to compute gradients in a neural network, and it is implemented in PyTorch using the Autograd package.

Code Examples

x = torch.empty(3)
# Creates an empty tensor with 3 elements
y = x.detach()
# Creates a new tensor that is not connected to the computation graph
weights = torch.tensor([1.0], requires_grad=True)
# Creates a tensor with a single element and requires gradient computation

x = torch.tensor([1.0], requires_grad=True)
y = x * 2
z = y * 3
z.backward()
# Computes the gradient of z with respect to x
print(x.grad)
# Prints the computed gradient

Lesson Summary

In this lesson, we learned how to install PyTorch and set up a development environment. We also learned about the basics of tensors in PyTorch, including how to create and manipulate them. Additionally, we introduced the Autograd package in PyTorch, which provides a way to compute gradients automatically. We saw how to use the Autograd package to compute gradients in a simple example. Finally, we touched on the concept of backpropagation, which is an algorithm used to compute gradients in a neural network. We will explore backpropagation in more detail in the next lesson.

Practice Exercise

Create a tensor with 5 elements and compute its gradient using the Autograd package. Then, use the detach() method to create a new tensor that is not connected to the computation graph.

What Is Next

In the next lesson, we will dive deeper into the Autograd package and learn how to use it to train a simple neural network. We will also explore more advanced topics, such as using the optim package to optimize model parameters and implementing custom loss functions.

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