Artificial Neural Networks Explained | Perceptron, MLP, Backpropagation, and Deep Learning
Artificial Neural Networks are the foundation of modern artificial intelligence and deep learning. In this video, we explain the basic principles of neural networks, starting from the perceptron and moving toward multi-layer perceptrons and deep learning models.
A perceptron works like a simple binary classifier. It receives weighted inputs, applies a threshold, and produces an output. This idea can be used to model simple decisions, such as predicting success based on study habits or class attendance.
As the problem becomes more complex, multi-layer perceptrons introduce hidden layers that help the model learn deeper patterns from data. To train these networks, the backpropagation algorithm is used to reduce prediction errors and improve performance.
In this video, you will learn:
What artificial neural networks are
How a perceptron works
Weighted inputs and threshold function
Perceptron as a logic gate and binary classifier
Introduction to multi-layer perceptrons
Role of hidden layers in neural networks
Backpropagation and error minimization
Deep learning and automatic feature extraction
Image recognition using neural networks
Vanishing gradient problem
Layer-wise pre-training for stable learning
This video is useful for students learning artificial intelligence, machine learning, deep learning, data science, and AI mathematics.
By the end of this lesson, you will understand how neural networks learn from data and how simple perceptrons developed into powerful deep learning architectures.
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