What you will learn

• Develop foundational deep learning skills by building, training, and evaluating PyTorch models you can showcase in your professional portfolio

• Gain practical experience with tensors, datasets, and automatic differentiation using PyTorch core tools including autograd and DataLoader

• Develop linear regression models using gradient descent, mini-batch optimization, and training/validation splits to evaluate model performance

• Apply cross-entropy loss, sigmoid-based classification, and advanced optimization techniques to build logistic regression models in PyTorch