Implementations and Quantization Notebooks of models for Edge AI!

Repository for the companion Colab notebook of the Domain-Specific Small Language Models book.

Practical examples and Python notebooks for optimizing Deep Learning models for deployment on embedded systems. Covers model quantization, pruning, and deployment strategies using TensorFlow, PyTorch, uTensor, and Edge Impulse.

Course-grade implementation and curated material for Fundamentals of Deep Learning and TinyML (MME 26849). Includes hands-on notebooks, slides, and practical experiments covering classical models (SVMs, perceptrons), modern deep nets (CNNs), and efficiency techniques (pruning, quantization) with a focus on size/latency-aware workflows

Implementation for the different ML tasks on Kaggle platform with GPUs.

This repository contains example notebooks and homeworks demonstrating various techniques in model optimization for Edge ML.

This repository contains example Jupyter notebooks demonstrating how to use the quantized versions of the PLLuM-8x7B-chat model in GGUF format

A collection of hand on notebook for LLMs practitioner

A Tutorial Notebook to Quantization in Machine Learning

Code snippets and notebooks used in PDS classes.

Here are Jupyter Notebooks that demonstrate pruning and quantization of the Fashion-MNIST Classification model in Pytorch. Also, using Torchscript, one can easily deploy these models on a C++ platform.