Introduction to Deep Learning in Tamil | Understanding Sigmoid Neurons & Gradient Descent

In this video, we take a deep dive into one of the most fundamental concepts in deep learning—the sigmoid function and sigmoid neuron. We begin by exploring the limitations of the Perceptron model, which operates on a harsh threshold logic, making it incapable of handling complex and non-linear patterns. This rigid nature prevents it from learning problems where smooth decision boundaries are needed.

To overcome these limitations, we introduce the sigmoid neuron, which uses the sigmoid activation function. Unlike the perceptron, the sigmoid neuron outputs values in a continuous range between 0 and 1, allowing for more flexible and effective learning. This smooth transition makes it possible to compute meaningful gradients, which is essential for optimizing neural networks.

A key part of training neural networks is the gradient descent algorithm, which we discuss in detail in this video. Gradient descent is a powerful optimization technique that helps adjust the parameters (weights and biases) of a neural network by minimizing the loss function. We explain how the algorithm works, how gradients are calculated, and why gradient descent is essential for deep learning models to learn from data.

This video is part of our Deep Learning in Tamil series, created especially for Tamil-speaking students, aspiring data scientists, and working professionals who want to master deep learning concepts in a simple and structured way. We break down these complex ideas with clear explanations, helping you build a strong foundation in AI and machine learning.

If you find this content valuable, don’t forget to like, share, and subscribe to our channel for more deep learning and AI tutorials in Tamil. Stay tuned for upcoming videos where we continue our journey into neural networks, activation functions, and advanced optimization techniques!

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