Embark on a journey to understand and build simple neural networks using PyTorch. This course explores neural networks, including essential concepts like layers, neurons, activation functions, and training a model. You’ll grasp these elements through progressive, interlocking code examples, culminating in the construction and evaluation of a simple neural network model for binary classification.

Learn to model the Wine dataset with PyTorch in this detailed course. Start by preprocessing the data for PyTorch, then construct and train a multi-class classification model. Explore model evaluation with various metrics and plots to identify strengths and improvements. The course concludes with methods to save and deploy your model, maximizing PyTorch's features for practical application.

Explore advanced PyTorch techniques to boost model performance. Learn about regularization, dropout to avoid overfitting, batch normalization for stable and quick training, and efficient training through learning rate scheduling. Also, discover how to save the best model with checkpointing. Each concise module offers practical skills to improve your machine learning projects.