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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 Python 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 the California Housing dataset for a regression task.

Syllabus

  • Unit 1: Building a Neural Network Library
    • Complete Namespace Declarations in Main Library Header
    • Creating the Main Library Header Interface
  • Unit 2: Modular Training Components
    • Define the XOR Dataset for Neural Network Training
    • Implementing the Complete Training Loop with Modular Components
    • Implementing Post-Training Evaluation for XOR Neural Network
  • Unit 3: Model Orchestration in C++
    • Implementing Model Constructor and Compile Method for Neural Network Orchestration
    • Implementing Abstract Methods for Model Interface
    • Implementing the Complete Neural Network Training Loop
    • Implementing the SequentialModel Class
    • Orchestrating Your Neural Network with the SequentialModel API
  • Unit 4: Preparing Real World Data
    • Loading and Preparing Housing Dataset for Neural Network Training
    • Implementing Data Splitting for Housing Dataset Preprocessing
    • Implementing Feature Scaling for Housing Dataset Preprocessing
  • Unit 5: California Housing Regression
    • Fixing Neural Network Architecture for California Housing Price Prediction
    • Building Your First Neural Network for Housing Price Prediction
    • Training Your Neural Network on California Housing Data

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