Building and Applying Your Neural Network Library

Overview

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

Syllabus

Unit 1: Modular Neural Networks JavaScript

Debugging Import Errors in Neural Network Package
Fix Missing Activation Function Exports in Neural Network Package
Creating the Main Package Index File

Unit 2: Modular Training Components

Define XOR Dataset for Neural Network Training
Implementing the Complete Training Loop with Modular Components
Post-Training Model Evaluation and Results Display

Unit 3: Model Orchestration Patterns

Building the Model Foundation: Constructor and Compile Methods
Implementing Abstract Methods and Predict Interface in Model Class
Implementing the Complete Neural Network Training Loop with the Fit Method
Implementing Sequential Model Architecture
Complete Neural Network XOR Problem Solution

Unit 4: Data Preparation for Neural Networks

Loading and Preprocessing the California Housing Dataset
Implementing Train-Test Data Splitting for Neural Network Preprocessing
Feature Scaling Implementation for Neural Network Data Preprocessing

Unit 5: California Housing Regression

Debugging Neural Network Architecture for Housing Price Prediction
Building Your Complete Neural Network - Final Integration
Training Your Neural Network on Real California Housing Data
Implementing Neural Network Evaluation Logic for California Housing Price Prediction