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The MLP Architecture: Activations & Initialization in R

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Overview

This course builds upon single layers to construct a complete Multi-Layer Perceptron (MLP). You'll learn to stack layers, explore different activation functions like ReLU and Softmax, and understand the importance of weight initialization for effective training.

Syllabus

  • Unit 1: The MLP Architecture: Activations & Initialization
    • Implementing Forward Propagation in a Multi-Layer Perceptron
    • Fixing Layer Dimensions in a Multi-Layer Perceptron
    • Building a Multi-Layer Perceptron Function in R
    • Expanding an MLP with an Additional Layer
    • Building a Multi-Layer Perceptron from Scratch in R
  • Unit 2: ReLU Activation and Flexible Layer Design in R MLPs
    • Fixing the ReLU Activation Function for Matrix Inputs
    • Implementing ReLU Activation in Your Neural Network
    • Implementing the ReLU Activation Function in R
  • Unit 3: Output Layer Activation Functions: Softmax and Linear in R MLPs
    • Implementing Numerically Stable Softmax in R
    • Verifying Softmax Outputs as Valid Probability Distributions
    • Implementing the Linear Activation Function for Regression Tasks
    • Debugging Output Activation Functions in Neural Networks
    • Building Neural Networks with Classification and Regression Output Activations
  • Unit 4: Weight Initialization Strategies for Neural Networks in R
    • Implementing Random Scaled Weight Initialization for Neural Networks
    • Fixing He Uniform Weight Initialization for Neural Networks
    • Implementing Xavier Normal Weight Initialization for Neural Networks
    • Implementing He Uniform Weight Initialization in Neural Networks

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