Binary Classification PyTorch | Real-World Example with Diabetes Dataset | Hands-on ML with PyTorch

Описание: In this tutorial from the Hands-on ML with PyTorch series, we dive into building a binary classification model using a real-world dataset — the Pima Indians Diabetes dataset.

We start with a detailed data loading and exploration phase using pandas, followed by data preprocessing with train-test splitting and normalization. You'll learn how to convert your dataset into PyTorch tensors, define a neural network with fully connected layers, use Binary Cross-Entropy Loss and the Adam optimizer, and track the training process over thousands of epochs.

We also evaluate the model’s accuracy and show you how to make predictions on new patient data. This end-to-end workflow demonstrates how you can solve real-world problems using PyTorch — one step at a time.

This video is perfect for beginners who want to understand how deep learning models are built, trained, and deployed using Python and PyTorch.

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