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Data Processing and Evaluation A freshly constructed neural network rarely delivers optimal results on its first run. Iterative refinement across both the dataset and the model architecture is required to achieve peak performance. This guide outlines a comprehensive strategy for tuning your PyTorch models. Dataset Partitioning Datasets are typi ...

Introduction to Classification Classification algorithms predict discrete categories rather than continuous values. Binary classification involves predicting between two posible outcomes, typically labeled as 0 and 1. Unlike linear regression, which can predict any numerical value, classification requires specialized algorithms like logistic re ...

Logistic Function Logistic regression is a generalized linear model, sharing many similarities with multiple linear regression. We define the logistic function (sigmoid) as: $$ g(z) = \frac{1}{1 + e^{-z}} $$ With $ z = \theta^T x $, the hypothesis becomes: $$ h_\theta(x) = \frac{1}{1 + e^{-\theta^T x}} $$ The graph of the logistic function is: ...

Training Deep Networks Why do we need batch normalization layers? Let us review some practical challenges that arise when training neural networks. First, the way data are preprocessed often dramatically influences the final result. Recall the example of using a multilayer perceptron to predict house prices. When working with real data, our fir ...