Explore PyTorch courses for tensors, training loops, and neural network projects, with guidance on Python prerequisites, GPUs, and model evaluation.
1 learning resource in this collection
PyTorch provides tools for tensor computation and neural network development. Learning it involves following data through a model, understanding gradients, and managing training and evaluation explicitly. Tensor shapes and device placement are practical concepts to learn early.
The Bourke and Neagoie bootcamp provides an extended practical route. Compare its pace with your Python experience and leave time for independent exercises. Check library and notebook dependencies, especially when a cloud environment supplies a different accelerator or package version.
Train a small classifier, save its parameters, and reload it for inference. Track loss and held-out performance, then inspect several incorrect predictions. Explain the difference between a training step and evaluation mode before adding a more complex architecture.
Udemy · Daniel Bourke and Andrei Neagoie
Explore Daniel Bourke and Andrei Neagoie’s PyTorch bootcamp for tensors, neural networks, training, and deployment, with Python and GPU notes.