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CodeSignal

Building and Applying Your Neural Network Library

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Overview

This course focuses on transforming your code into a reusable R package and applying it to a real-world problem. You'll refactor your existing components into a structured package, build a Model R6 class for easier network definition and training, and finally, train your neural network on the Boston Housing dataset for a regression task.

Syllabus

  • Unit 1: Building Neural Networks in R
    • Debugging Import Errors in a Modular Neural Network Package
    • Export Activation Functions and Derivatives in R Package
    • Creating the Main Package Initialization File for a Neural Network Library in R
  • Unit 2: Modular Training Components
    • Define the XOR Dataset for Neural Network Training in R
    • Implementing the Training Loop for a Modular Neural Network in R
    • Post-Training Evaluation and Results Table for XOR Neural Network
  • Unit 3: Model Orchestration in R
    • Implementing Model Initialization and Compilation Functions in R
    • Defining Abstract Methods and the Predict Interface in an R Model Class
    • Implementing the fit Method for Neural Network Training in R
    • Implementing the SequentialModel Class in R
    • Solving the XOR Problem with a Custom Neural Network in R
  • Unit 4: Preparing Real World Data
    • Loading and Inspecting the California Housing Dataset in R
    • Splitting the Dataset into Training and Testing Sets in R
    • Feature Scaling for Neural Network-Ready Data in R
  • Unit 5: Real World Neural Network Application
    • Fixing Neural Network Output Activation for Regression in R
    • Build and Train a Neural Network for House Price Prediction in R
    • Train Your Neural Network on California Housing Data
    • Evaluate Neural Network Regression Performance on Scaled and Original Data

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