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Introduction to Data Science (DS 215)
·
Module 12
Introduction to Neural Networks
1
Perceptron Learning Rule Derivation
Derive the weight update rule for the Perceptron algorithm.
2
From Perceptrons to MLPs: Overcoming Limitations
Explain the limitations of the Perceptron and how Multi-Layer Perceptrons (MLPs) overcome them.
3
Backpropagation: The Chain Rule in Action
Derive the backpropagation algorithm for a simple MLP using the chain rule of calculus.
4
The Universal Approximation Theorem
Explain the Universal Approximation Theorem and its implications for neural networks.
5
Neural Networks for Digit Classification
Project: Build, train, and evaluate a simple neural network for digit classification using Scikit-learn.
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