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Building and Applying Your Neural Network Library

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Details

Provider

CodeSignal
Pricing

Free Certificate
Languages

English
Certificate

Certificate Available
Duration & workload

2 hours
Sessions

On-Demand
Level

Intermediate

Found in

Part of

Neural Networks from Scratch using Python and Numpy

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: Modularizing Core Components: Layers and Activations

Debugging Import Errors in Neural Networks
Debugging Import Errors in Neural Networks
Building Your Neural Network Package API

Unit 2: Modularizing Training Components: Losses and Optimizers

Defining the XOR Training Dataset
Implementing the Complete Training Loop
Evaluating Your Trained Neural Network

Unit 3: Orchestrating Your Network: The Model Class

Building Your Model Orchestra Conductor
Defining Abstract Methods for Model Interface
Orchestrating the Complete Training Pipeline
Building Your Sequential Model Architecture
Solving XOR with Your Neural Orchestra

Unit 4: Data Handling: Preparing the California Housing Dataset

Loading Real World Housing Data
Splitting Data for Machine Learning
Feature Scaling for Neural Networks

Unit 5: Application: Training and Evaluating on California Housing

Fixing Neural Networks for Housing Prediction
Building Neural Networks for Housing Prediction
Debugging Neural Networks for Housing Prediction
Evaluating Neural Networks for Housing Prediction

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