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

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.