galafis/Neural-Network-Framework

Professional project by Gabriel Demetrios Lafis

What's novel

Professional project by Gabriel Demetrios Lafis

Code Analysis

0 files read · 5 rounds

A NumPy-based neural network framework implementing dense layers, activations, losses, and optimizers via a sequential API.

Strengths

Clean separation of concerns with distinct modules for layers, activations, losses, and optimizers. The README provides a clear architectural diagram and usage instructions. Standard naming conventions are followed.

Weaknesses

Unable to verify core mathematical logic (backpropagation, Adam optimizer) due to system constraints preventing reading of .py files. Error handling is likely minimal as is typical for NumPy-based ML libraries. Test coverage cannot be fully assessed without seeing the test file content.

Score Breakdown

Innovation

3 (25%)

Craft

59 (35%)

Traction

6 (15%)

Scope

61 (25%)

Signal breakdown

Innovation

Not Fork+1

Code Novelty+1

Concept Novelty+0

Craft

Ci-2

Tests+5

Polish+1

Releases+0

Has License+5

Code Quality+16

Readme Quality+15

Recent Activity+7

Structure Quality+5

Commit Consistency+2

Has Dependency Mgmt+5

Traction

Forks+0

Stars+6

Hn Points+0

Watchers+0

Early Traction+0

Devto Reactions+0

Community Contribs+0

Scope

Commits+5

Languages+8

Subsystems+5

Bloat Penalty+0

Completeness+7

Contributors+5

Authored Files+8

Readme Code Match+3

Architecture Depth+3

Implementation Depth+8

Evidence

Commits

Contributors

Files

Active weeks

TestsCI/CDREADMELicenseContributing

Repository

Language

Python

Stars

Forks

License

MIT