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ai_curriculum

Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford, MIT, UC Berkeley.

aifes_for_arduino

This is the Arduino® compatible port of the AIfES machine learning framework, developed and maintained by Fraunhofer Institute for Microelectronic Circuits and Systems.

appendix

This repository holds the Appendix Documents for the EdX TinyML Specialization

as73211-colorimeter-stm32f7

Demo project for ams AS73211 XYZ sensor with mikromedia+ for STM32F7

atlas-scientific-ezo-python

audio_classification_cnn_esc-50

Audio classification model which uses CNN to train ESC-50 dataset.

audio_classification_keras

Audio classification using Keras with ESC-50 dataset.

awesome-project-ideas

Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

coral_usb_ml_accelerator

Testing Google Coral USB Accelerator's performance with 04 models of Raspberry Pi. Results have shown 10 times faster inferencing speeds with the Coral hardware attached.

esc-50

ESC-50: Dataset for Environmental Sound Classification

from-0-to-research-scientist-resources-guide

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

generative-ai-for-beginners

12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning

inwatersense

Ontologies for Intelligent Water Quality Management through Wireless Sensor Networks

kapre

kapre: Keras Audio Preprocessors

l3xz_openmv_camera

ROS driver for OpenMV camera.

llm-finetuning

LLM Finetuning with peft

mdpi-water-smart-water-solutions-with-big-data

Material for the article published in this journal

micropython_as7262x_driver

MicroPython driver for AS7262/AS7263 nano spectrometer sensor

minbpe

Minimal, clean, educational code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.

ml-youtube-courses

📺 Discover the latest machine learning / AI courses on YouTube.

mnist-using-k-means

It is One of the Easiest Problems in Data Science to Detect the MNIST Numbers, Using a Classification Algorithm, Here I have used a csv File which contains the Pixels of the Numbers from 0 to 9 and we have to Classify the Numbers Accordingly. I have Used K-Means Classification Algorithm.

models

Models and examples built with TensorFlow

models-1

Model Zoo for Intel® Architecture: contains Intel optimizations for running deep learning workloads on Intel® Xeon® Scalable processors

nn-zero-to-hero

Neural Networks: Zero to Hero

optml_course

EPFL Course - Optimization for Machine Learning - CS-439

plotneuralnet

Latex code for making neural networks diagrams

prompt-engineering-guide

🐙 Guides, papers, lecture, notebooks and resources for prompt engineering

pytorch-deep-learning

Deep Learning (with PyTorch)

r-pi-database-