The perceptron is a fundamental concept in deep learning, with many algorithms stemming from its original design. In this tutorial, I’ll show you how to build both single layer and multi-layer perceptrons (MLPs) across three frameworks: Custom class...

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A comprehensive guide to building multilayer perceptrons (MLPs) for binary classification using three approaches: custom Python implementation, scikit-learn's MLPClassifier, and Keras Sequential models. The tutorial covers fundamental concepts like activation functions, loss functions, and optimization algorithms (SGD vs Adam), then demonstrates practical implementation through a fraud detection project. It includes detailed explanations of forward propagation, backpropagation, and techniques for handling imbalanced datasets using SMOTE, class weights, and regularization methods.

Learn to Build a Multilayer Perceptron with Real-Life Examples and Python Code

How to Build an MLP Classifier with SGD Optimizer

How to Build an MLP Classifier with Adam Optimizer