Lecture 59: Perceptron Algorithm Implementation
In this video, we delve into the Perceptron Algorithm, a foundational concept in machine learning. We explore the Perceptron Trick, a method where the decision boundary is moved slightly (a baby step) towards each misclassified point. This is achieved by updating the weights by an amount equal to the learning rate times the feature values.
What You'll Learn:
Perceptron Algorithm: Understand the basics and how the decision boundary is adjusted.
Stopping Criteria: Learn when and how to stop the algorithm effectively.
Linear Separable or Not?: Determine if your data can be separated linearly.
Batch/Mini-batch/Stochastic Gradient Descent: Discover the differences and implementations of these gradient descent methods.
Python Implementation: See the algorithm in action with practical Python code examples for Stochastic, Mini-batch, and Batch versions.
This lecture is perfect for anyone looking to deepen their understanding of machine learning fundamentals and Python implementation. Don't forget to like, share, and subscribe! 👍
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#PerceptronAlgorithm #MachineLearning #PythonProgramming #GradientDescent #AI #DataScience #ML #DeepLearning #TechEducation #Stochastic #MiniBatch #BatchGradientDescent
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