How Perceptrons Work | Artificial Neuron | Programming Perceptron in Python | OR Gate Programming
Welcome to this comprehensive lecture on Perceptrons — one of the foundational concepts in the field of Artificial Neural Networks (ANNs).
In this video, we explore the theoretical underpinnings of the Perceptron model and then walk through its implementation in Python step by step.
Whether you're a beginner in machine learning or someone brushing up on core concepts, this lecture will provide you with a clear, intuitive understanding of how perceptrons work and how they form the building blocks of more complex neural networks.
🔍 What You’ll Learn:
The origin and significance of the Perceptron Algorithm
Mathematical formulation: weighted sum, bias, activation function
How Perceptrons learn: gradient descent & update rules
Linearly separable data and limitations of single-layer perceptrons
Hands-on implementation of a perceptron in Python
Code walkthrough with explanations of key steps
Sample dataset and training/testing demonstration
🧠 Who This Video is For:
Students of Computer Science, AI, or Data Science
Anyone new to Machine Learning or Neural Networks
Educators looking for a teaching resource
Developers and researchers brushing up on fundamentals
🛠️ Technologies Used:
Python 3.x
NumPy
Matplotlib (for optional visualization)
By the end of this lecture, you’ll not only understand the concept of a perceptron but also be able to code and train your own simple model from scratch in Python — no frameworks needed!
📌 Don’t forget to like, share, and subscribe if you found this helpful — and feel free to drop your questions in the comments.
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