Introduction to Linear Layers in Neural Networks with PyTorch

The Linear module in PyTorch is fundamental for creating neural network layers that perform linear transformations. It accepts parameters: in_features and out_features which denote the size of the input and the output, respectively. Linear layers are crucial in constructing neural networks as they map incoming data to a desired output space using learned weights and biases. This process is essential for tasks like regression, classification, and feature learning. Utilizing torch.nn.Linear, one can easily integrate linear layers into models, fostering both simplicity and flexibility in architecture design.
This Python script illustrates how to implement a basic neural network with a single linear layer in PyTorch. It covers the initialization of the layer, constructing the model, and performing a forward pass using a sample input tensor.
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Видео Introduction to Linear Layers in Neural Networks with PyTorch канала Donutloop