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Chapter 3: Advanced Concepts

From "Machine Learning Basics"

Wikipedia: Neural Networks

https://en.wikipedia.org

Lecture 5: Backpropagation

MIT OpenCourseWare

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Introduction

Neural networks are computing systems inspired by biological neural networks. They consist of interconnected nodes (neurons) that process information using a connectionist approach to computation.

Key Components

  • Input Layer: Receives the initial data
  • Hidden Layers: Where computation happens
  • Output Layer: Produces the final result
  • Weights: Parameters that determine signal strength
  • Activation Function: Determines if a neuron should be activated

Learning Process

The network learns by:

  1. Processing input data (forward propagation)
  2. Comparing output to expected result (loss calculation)
  3. Adjusting weights to minimize error (backpropagation)
  4. Repeating until satisfactory performance is achieved

Common Architectures

Type Use Case Example
Feedforward Basic classification MNIST digit recognition
CNN Image processing ImageNet classification
RNN Sequence data Language translation

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