CNN Explained in Tamil | Connect Convolution with Neural Network + Trainable Params | Adi Explains

Welcome to another exciting episode in our Deep Learning Series in Tamil! In this video, we explore one of the most fundamental concepts in deep learning — how convolution layers are connected with neural networks to form a Convolutional Neural Network (CNN). This lesson is delivered completely in Tamil, making it easier for Tamil-speaking students, engineers, and AI enthusiasts to understand complex deep learning topics in their own language. If you've ever wondered how CNNs work under the hood or how the convolution operation is actually integrated into a neural network, this video is for you. We start by revisiting the core idea behind convolution — why we use it, how it extracts features from images, and how filters (or kernels) move across the input data. Then, we connect this knowledge with fully connected layers of a traditional neural network, illustrating how the output from convolution layers flows into the dense layers for classification or regression tasks. By the end of the video, you’ll have a clear picture of how convolutional layers and neural networks work together to create a full CNN architecture. What makes this video even more valuable is that we also walk you through how to calculate the number of trainable parameters in a CNN. This is a crucial skill for anyone building or analyzing deep learning models. Many students and beginners in AI struggle with understanding how to count weights and biases layer-by-layer. This video makes it simple by showing practical examples and easy-to-follow formulas — all in Tamil — so that you not only understand the concept but can also apply it in your own projects. This video is part of the "Deep Learning in Tamil" series, specially designed for Tamil medium students, college learners, self-taught developers, and professionals who are entering the world of machine learning and artificial intelligence. We strongly believe that language should never be a barrier when it comes to learning advanced technologies