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

Understanding loss functions is a crucial part of learning how neural networks work, and in this video, we take a deep dive into one of the most important loss functions used in binary classification problems — Binary Cross Entropy. Delivered entirely in Tamil, this video is perfect for beginners as well as intermediate learners who want a clear, intuitive understanding of the concept, without being overwhelmed by complex mathematics. In this video, I begin by explaining why we use Binary Cross Entropy in the first place. Often, when we build neural networks for binary classification tasks — such as spam vs. not spam, cat vs. dog, or healthy vs. unhealthy — we need a loss function that can accurately measure how far off our model's predictions are from the actual labels. That’s where Binary Cross Entropy comes in. It provides a powerful way to penalize wrong predictions in a way that encourages the model to become more confident and accurate over time. Once the intuition behind Binary Cross Entropy is laid out, I move on to how it works inside a neural network. I walk through the actual formula, break it down part by part, and explain the meaning of each term in simple Tamil. I’ve also included a step-by-step numerical example where we calculate the Binary Cross Entropy Loss manually for a single prediction. This helps solidify the concept and shows how even a small difference between predicted and actual values affects the loss. Another highlight of the video is the explanation of how Binary Cross Entropy is differentiable and works smoothly with the backpropagation algorithm in neural networks. I also touch upon the role of the sigmoid activation function in binary classification and why Binary Cross Entropy pairs so well with it. The connection between probabilities, log values, and entropy is explained in a practical and relatable way, ensuring that even those from non-mathematical backgrounds can grasp the concept with ease. Throughout the video, the focus