Скачать или смотреть Project Name : Implement Softmax Regression & Classification with Linear Neural Networks

Project Name : Implement Softmax Regression & Classification with Linear Neural Networks

The objective of this project is to understand and implement Softmax Regression, a foundational linear model for classification problems.

Goal
Implement Softmax Regression both manually and concisely using PyTorch.
Understand forward pass, loss functions, and optimization.
Measure model performance with accuracy.
Discuss model generalization and adaptation under different data distributions.

What is Softmax Regression?
Softmax Regression is the classification version of Linear Regression.

While Linear Regression outputs a single continuous value, Softmax Regression outputs probabilities for each class.
It is commonly used for multi-class classification tasks.
Softmax Regression:

It extends linear regression to classification by outputting probabilities for multiple classes.
Uses the softmax activation to map raw outputs (logits) to probability distributions.
The class with the highest probability is chosen as the prediction.
PyTorch from Scratch vs Concise:

From Scratch: We manually implemented softmax, cross-entropy loss, and parameter updates.
Concise: Used nn.CrossEntropyLoss and built-in modules like nn.Linear to simplify model training.

Why Use Softmax Regression?
It’s simple yet effective for multi-class classification.
Offers probabilistic interpretation.
Often used as a baseline classifier before trying complex models like CNNs or transformers.

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