Perceptron | Neural Networks Explained | Introduction to Deep Learning | Tamil Tutorial

Deep learning is transforming the world of artificial intelligence, but where did it all begin? In this video, we explore the evolution of neural networks, starting from the McCulloch-Pitts neuron to the perceptron and finally to the multi-layer perceptron (MLP). This is a must-watch for Tamil-speaking students and professionals looking to build a strong foundation in deep learning. The McCulloch-Pitts neuron was one of the earliest attempts to model how a brain processes information. However, it had significant drawbacks—it could only solve very simple problems and failed at handling more complex patterns. This led to the development of the perceptron, a more advanced model capable of learning from data. But even the perceptron had its limitations. It could only classify linearly separable functions, meaning it struggled with problems that required more complex decision boundaries—like the XOR gate. The XOR function is non-linearly separable, meaning a simple perceptron cannot solve it. This limitation highlighted the need for multi-layered architectures, which led to the development of the multi-layer perceptron (MLP). In this video, we explain how the multi-layer perceptron (MLP) overcomes these challenges by introducing a hidden layer between the input and output layers. This additional layer enables neural networks to handle complex patterns, making them capable of solving problems like XOR. This transition from simple perceptrons to deep neural networks marks a significant leap in artificial intelligence. Through this discussion, you will understand: 1. Why the McCulloch-Pitts neuron failed 2. What a perceptron is and how it works 3. The difference between linearly separable and non-linearly separable functions 4. Why the XOR gate is non-linearly separable 5. How multi-layer perceptrons (MLPs) solve complex problems This video is part of our Deep Learning series in Tamil, designed for Tamil-speaking students and professionals who want to master artificial