
Train an AI with PyTorch to learn the linear function y = 2x + 1 using tensors for input and output data, weights, and biases, and test the results.
Learn how PyTorch optimizers, starting from random weights and biases, update parameters using stochastic gradient descent, with learning rate effects and the math of loss and gradient updates.
Engage in the forward pass by computing y_hat from inputs X with a model's weight and bias, then observe how predictions evolve during training as these parameters are optimized.
Compute the loss function in PyTorch using mean squared error (MSE), averaging the squared differences between y_pred and y, illustrated with a numeric example.
Explore increasing a linear layer's output, using dropout as a regularization technique to combat overfitting. See a four-layer network with linear, ReLU, and dropout, and a basic SGD training loop.
Build a PyTorch model with a linear1 (1→10), a ReLU, and a linear2 (10→1). Train with SGD (lr 0.01) and MSE loss for 10,000 iterations, then inspect weights, biases, and predictions.
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