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Modeling the Wine Dataset with PyTorch

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Overview

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.

Syllabus

Unit 1: Preprocessing the Wine Dataset for PyTorch

Loading the Wine Dataset
Splitting the Wine Dataset
Wine Dataset Feature Scaling
Converting Data to PyTorch Tensors
Preprocessing Wine Data for PyTorch

Unit 2: Building a Multi-Class Classification Model with PyTorch

Building a Classification Model
Changing Layers of PyTorch Model
Debugging PyTorch Model Training
Complete the PyTorch Model
Defining and Training a Model

Unit 3: Deep Model Evaluation with PyTorch

Evaluating Model Performance in PyTorch
Visualizing Model Performance with More Training
Debugging PyTorch Model Evaluation
Complete PyTorch Model Evaluation
Evaluate and Visualize Model Performance

Unit 4: Saving and Loading PyTorch Models

From Model Training to Saving and Loading
Modifying Configuration and Descriptive Model Saving
Debugging Model File Saving
Save and Load Your Model
From Training to Deployment with Save and Load

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