How neural networks turn inputs into predictions, how they learn with gradient descent, and how to train one in PyTorch.
Neural networks power modern AI, from image recognition to language models. This course builds the core ideas from a single neuron up to a trained network, then puts them into practice with PyTorch on the wine quality dataset from the hub.
Install with pip install torch pandas scikit-learn.
4 lessons · 1 hr 6 min
Weighted sums, activation functions and layers.
Loss functions, gradients, backpropagation and the learning rate.
A complete training loop on the wine quality data.
Overfitting, dropout, early stopping and when to use a pretrained model.
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