Is this skill compatible with standard neural networks?

While designed for ternary and quantized networks, the discrete optimization principles can be applied to any parameter-based system where traditional backpropagation fails.

What is Discrete Backprop?

Discrete Backprop is a gradient-free learning method that estimates parameter updates using discrete perturbations—specifically trits {-1, 0, +1}—instead of continuous floating-point gradients.

Why use trit-based learning?

Trit-based learning is highly efficient for hardware because it replaces complex multiplications with simple additions and subtractions, and it works on functions that aren't mathematically differentiable.

Can this skill handle high-dimensional models?

Yes, it includes a Simultaneous Perturbation implementation (similar to SPSA) that can estimate gradients for all dimensions using only two loss evaluations per step.

Discrete Backprop

Name: Discrete Backprop
Author: plurigrid

byplurigrid

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Data Science & ML

Implements gradient-free optimization and ternary learning using discrete trit-based perturbations for non-differentiable systems.

About

The Discrete Backprop skill enables Claude to perform machine learning and optimization in environments where traditional continuous gradients are unavailable or computationally expensive. By utilizing a trit-based approach {-1, 0, +1}, it facilitates the training of quantized ternary neural networks, optimizes non-differentiable functions like hash lookups or conditional logic, and solves complex combinatorial problems. This skill is ideal for developers working on hardware-efficient AI, neuromorphic computing, or GF(3) systems that require native discrete learning patterns.

Key Features

2 GitHub stars
Gradient-free optimization for non-differentiable and discrete functions
Native GF(3) conservation logic for balanced parameter updates
Ternary Neural Network (TNN) implementation for multiplier-less inference
Simultaneous Perturbation (SPSA) for efficient high-dimensional gradient estimation
Support for quantized learning in binary and ternary systems

Use Cases

Optimizing software logic involving branching and non-differentiable conditionals
Training ultra-low-power AI models for FPGA or edge hardware deployment
Solving discrete combinatorial optimization problems in search and logistics

About

Key Features

2 GitHub stars
Gradient-free optimization for non-differentiable and discrete functions
Native GF(3) conservation logic for balanced parameter updates
Ternary Neural Network (TNN) implementation for multiplier-less inference
Simultaneous Perturbation (SPSA) for efficient high-dimensional gradient estimation
Support for quantized learning in binary and ternary systems

Use Cases

Optimizing software logic involving branching and non-differentiable conditionals
Training ultra-low-power AI models for FPGA or edge hardware deployment
Solving discrete combinatorial optimization problems in search and logistics