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The MLP Architecture: Activations & Initialization in JavaScript

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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
    • Fix Layer Dimensions in a Multi-Layer Perceptron
    • Building a Multi-Layer Perceptron (MLP) Class from Scratch
    • Adding a Fourth Layer to a Multi-Layer Perceptron
    • Building a Multi-Layer Perceptron from Scratch
  • Unit 2: ReLU Activation and Flexible Activation Functions in MLPs
    • Fix the ReLU Activation Function for Neural Networks
    • Implementing ReLU Activation Function in Neural Network
    • Implement the ReLU Activation Function for Neural Networks
  • Unit 3: Output Layer Activation Functions: Softmax and Linear in MLPs
    • Implement Numerically Stable Softmax Function
    • Verifying Softmax Outputs Sum to One
    • Implementing Linear Activation Function for Neural Network Regression
    • Debugging Neural Network Output Activations
    • Building Classification and Regression Neural NetworksAI
  • Unit 4: Weight Initialization Strategies for MLPs in JavaScript
    • Implement Random Scaled Weight Initialization for Neural Network
    • Fix the He Uniform Weight Initialization in Neural Network
    • Implementing Xavier Normal Initialization for Neural Networks
    • Implement He Uniform Weight Initialization for Neural Networks

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