Introduction to PyTorch
Key concepts and skills covered
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Basics of PyTorch, understanding what tensors are, and how to
perform basic operations on them.
- Usage of torch functions like empty, zeros, ones, and randn.
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Understanding of automatic differentiation mechanism provided by
PyTorch’s Autograd.
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Understanding the concept of a Linear Layer in a neural network.
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Building a simple linear regression model using PyTorch tensors and
Autograd.
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Training a simple linear regression model, including defining a loss
function, an optimization function, and updating weights through
backpropagation.
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Understanding how to pass data in batches to a model during
training.
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Understanding basics of activation functions and how to use them in
neural network
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Saving and loading PyTorch models, understanding the difference
between saving the whole model and just the state dictionary.
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Understanding the concept of Transfer Learning, how to use
pretrained models in PyTorch.
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Learning to modify a pre-trained model for a new task (fine-tuning).
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Recognizing how to use different datasets and how to apply
transformations on them using torchvision.transforms.
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Understanding how to evaluate a model by making predictions on test
data.
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Simplifying complex neural networks using nn.Sequential in PyTorch.
- Visualizing a neural network
- Visualizing PyTorch model, data and training with TensorBoard