CNN Explained in Tamil | Weight Sharing, Layers & Training Explained | Deep learning in Tamil

Welcome to another exciting and informative video from Adi Explains, where we break down complex topics in a simple and relatable way — in Tamil. In this video, we dive deep into one of the most fundamental building blocks of modern deep learning — the Convolutional Neural Network (CNN). Whether you're a student, an aspiring machine learning engineer, or a working professional looking to strengthen your understanding of deep learning, this Tamil explanation of CNN will help you grasp every important concept clearly and intuitively. What is a CNN, and why is it so important in AI today? CNNs are the backbone of many computer vision applications — from facial recognition and self-driving cars to medical image diagnosis and even artistic style transfer. In this video, you'll not only learn what a CNN is but also why it's used, and how it works under the hood. The content is delivered in Tamil, ensuring that native Tamil speakers can learn these advanced AI concepts comfortably in their own language. We begin by introducing the basic structure of a Convolutional Neural Network — including convolutional layers, filters, activation functions, and pooling layers. We explain how an image is processed layer by layer, and how important features like edges, textures, and shapes are extracted. We then move on to one of the key concepts in CNNs — weight sharing. This technique is what makes CNNs so efficient and powerful. You'll understand how a single filter (or kernel) scans across the entire image and shares the same weights, drastically reducing the number of parameters compared to a fully connected neural network. We also take a closer look at how to train a CNN. Concepts like forward propagation, backpropagation, loss functions, gradient descent, and optimization are covered in a way that connects to real-world analogies and visual examples — all explained in Tamil for better relatability and understanding. You’ll also get insights into practical tips on training deep C