Model Optimization Techniques

This repository contains implementations of various model optimization techniques, including model pruning and quantization, to improve the efficiency of machine learning models.

Introduction

Machine learning models can be resource-intensive. Model optimization techniques are essential to reduce the computational requirements while maintaining performance. This repository gathers implementations of popular optimization techniques that can be applied to various machine learning models.

Features

• Model Pruning: Reduce model size by eliminating unimportant weights or neurons.
• Model Quantization: Reduce memory and computation usage by representing weights in fewer bits.
• TFLite Compression