Neural Network Project in Tamil | Credit Card Fraud Detection with Deep Learning | Adi Explains

Are you a Tamil-speaking student or aspiring data scientist looking to understand how neural networks work in real-world projects? In this in-depth Tamil tutorial, we take a practical approach to learning deep learning by building a Credit Card Fraud Detection system using Neural Networks from scratch. In this video, you'll get a complete walkthrough of implementing a deep learning model for fraud detection, fully explained in Tamil. We begin with the basics of how a neural network functions, why it’s suitable for fraud detection problems, and how to structure your code in Python using libraries like TensorFlow and Keras. Whether you're new to AI or already familiar with machine learning, this video will give you a deeper understanding of how neural networks are trained and optimized in real-world scenarios. You will learn how to prepare and clean your dataset, what features are important in fraud detection, and how to handle imbalanced data (a common challenge in fraud datasets). We also explain crucial concepts like epochs, batch size, activation functions, and how the model learns through backpropagation. The project uses real-world data to help you understand how these techniques are applied in practice, not just in theory. This Tamil tutorial is perfect for those who prefer to learn technical topics in their mother tongue. Every concept is explained clearly, step-by-step, so even beginners can follow along and build their own neural network-based projects by the end of the video. You’ll also understand how to monitor model accuracy, prevent overfitting using techniques like dropout, and evaluate your model's performance using metrics like precision, recall, and F1-score. The Credit Card Fraud Detection project is not just a great way to learn about neural networks, but also a valuable addition to your data science portfolio. This kind of project is often asked in interviews, and understanding it deeply will help you explain it confidently during placements