Cross Entropy Loss Function in Deep Learning | Deep Learning in Tamil | Adi Explains

Welcome to this in-depth Tamil tutorial on one of the most fundamental concepts in deep learning — the Cross Entropy Loss Function. In this video, I explain what cross entropy loss is, why it’s used in classification problems, and how it works mathematically with an example problem. This video is designed for Tamil-speaking students, professionals, and deep learning enthusiasts who want to build a strong foundation in machine learning and AI. Cross entropy is one of the most widely used loss functions in neural networks, especially in tasks like image classification, natural language processing, and other multi-class classification problems. Understanding the cross entropy loss function is crucial because it directly impacts how your deep learning model learns from data. In this tutorial, I take you through the mathematical intuition behind cross entropy step-by-step and show how the function behaves with real values. This video is not just theoretical — it includes a fully worked-out example problem, where we compute the cross entropy loss between the predicted probabilities of a neural network and the actual labels. If you're struggling to understand how the loss function penalizes incorrect predictions or how logarithmic functions play a role in learning, this tutorial will clear all your doubts in your own language. Whether you're a beginner trying to understand the basics of neural networks or an intermediate learner aiming to strengthen your understanding of deep learning concepts in Tamil, this video will be a great resource. I explain each part of the formula — from softmax output to the log loss — and also discuss why cross entropy is preferred over mean squared error for classification tasks. Through this Tamil explanation, I ensure that even those with limited math background can follow along and grasp the underlying concepts. The tutorial focuses on: Why we use cross entropy for classification The relationship between cross entropy and softmax How