Two Hidden Layer Neural Network for Fraud Detection

In this video, you'll learn how a two hidden layer neural network works through a practical fraud detection example. Instead of explaining only the theory, we walk through a real banking transaction and show how a neural network processes multiple features to predict whether a transaction is fraudulent or genuine.

Starting with transaction details such as transaction amount, transaction time, merchant risk score, foreign transaction, device change, transaction frequency, distance from home, and card age, we follow the complete journey through the input layer, two hidden layers, and the output layer.

You'll also understand:
What happens inside Hidden Layer 1
What Hidden Layer 2 learns
How the ReLU activation function introduces non-linearity
Why the Sigmoid activation function is used in the output layer
How fraud probability is calculated
Why deep neural networks outperform simple machine learning models for complex pattern recognition

👨‍🏫 About the Presenter

Hi, I'm Pankaj Sharma (PM Expert), a trainer and consultant with over 25 years of experience in Project Management, Artificial Intelligence, Business Analysis, Product Management, and Cybersecurity. My goal is to simplify complex concepts using practical, real-world examples that are easy to understand.

📚 Who Should Watch?

Students learning Artificial Intelligence
Machine Learning Beginners
Deep Learning Enthusiasts
Data Science Professionals
Banking & Financial Analytics Professionals
Interview Preparation Candidates
AI Certification Aspirants

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Видео Two Hidden Layer Neural Network for Fraud Detection канала PM Expert