Activation Function in Neural Network Explained Part 1| Deep Learning| Machine Learning|GATE2026

Описание: In this lecture, we explain the concept of Activation Functions, one of the most important components of Neural Networks and Deep Learning. Activation functions determine whether a neuron should activate by transforming the weighted sum of inputs into an output.

We discuss how activation functions introduce non-linearity, enabling neural networks to learn complex patterns that cannot be captured by simple linear models. This lecture also explains how activation functions influence gradient flow, help avoid saturation, and shape decision boundaries during training.

The session is designed with a strong conceptual focus and is especially useful for students preparing for GATE, UGC NET, and university-level Deep Learning courses.

✨ Topics Covered in This Video:

What is an activation function?

Importance of non-linearity in neural networks

Role of activation functions in learning complex patterns

Gradient flow and saturation issues

Decision boundary shaping

Universal Approximation Theorem and activation functions

🎯 Who Should Watch This Video?

Students learning Deep Learning and Neural Networks

GATE and UGC NET aspirants

UG & PG Computer Science students

Anyone building a strong foundation in AI and Machine Learning
#deeplearning #machinelearning #artficialintelligence #deeplearn #neuronet #activationfunction

📘 This lecture lays the groundwork for understanding popular activation functions such as Sigmoid, Tanh, ReLU, and Softmax in upcoming videos.

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