Understanding Multi Layer Perceptron in Deep Learning |GATE 2026|UGCNET 2026

Описание: In this video, we explain the Multi-Layer Perceptron (MLP), one of the most fundamental architectures in Deep Learning.
An MLP is a feed-forward neural network consisting of an input layer, one or more hidden layers, and an output layer, trained using forward propagation and backpropagation.

This lecture focuses on the definition, structure, and learning mechanism of MLPs, making it ideal for GATE, UGC NET, and university-level courses in AI / Machine Learning / Deep Learning.

⏱️ Chapters (3:56 mins)

00:00 – 00:40
Introduction to Multi-Layer Perceptron (MLP)

00:40 – 01:30
Components of MLP: Input, Hidden, and Output Layers

01:30 – 02:20
Neuron Computation: Weights, Bias, and Activation Functions

02:20 – 03:10
Forward Pass in MLP

03:10 – 03:40
Backward Pass & Gradient Descent (Backpropagation)

03:40 – 03:56
Summary & Key Takeaways

🎯 Who Should Watch

Deep Learning beginners

GATE 2026 aspirants

UGC NET 2026 (Computer Science) candidates

UG & PG students of AI / ML
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📘 This video builds the foundation for understanding deep neural networks and prepares you for advanced topics like backpropagation, CNNs, and Transformers.

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