Building and Applying Your Neural Network Library

Übersicht

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.

Lehrplan

• 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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Fachgebiete

Artificial Intelligence