D

deep-learning

The AI and the Automobile collection brings together multiple specifications that define how AI integrates into modern vehicle systems, treating the automobile as a software-defined platform where intelligence spans perception, control, navigation, and energy management. It emphasizes real-time performance, safety-critical architecture, and the transition from purely mechanical systems to continuously evolving software-driven mobility. Across the collection, the specs also describe how automotive AI should be built through open and interoperable systems with strong safety practices, including simulation, validation pipelines, and human-in-the-loop oversight to handle edge cases. Together, they frame vehicle intelligence as a layered system combining autonomy, redundancy, diagnostics, and secure update mechanisms to enable reliable and scalable deployment. https://roxanneardary.com/ai-and-the-automobile/

Bu proje, kapalı ve siyah hazneli bir geri dönüşüm kutusunda atıkları otomatik sınıflandıran bir derin öğrenme modeli geliştirmek için hazırlanmıştır. Hedef sınıflar: plastik, cam, kağıt, metal, karton ve çöp.

BIL635 - Modified Version of "Finding a Target with Zero-shot Invariant and Efficient Visual Search"

BIL635 - Modified Version of "Finding a Target with Zero-shot Invariant and Efficient Visual Search" v2

PyTorch reimplementation of CheXNet: multi-label classification and CAM/Grad-CAM localization of 14 thoracic diseases on ChestX-ray14, with full-resolution (1024px) ResNet50 training.

Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)

GPU-accelerated embedding server for RAG systems - CUDA, FastAPI, sentence-transformers | Serveur d'embeddings GPU ultra-rapide

https://ayinedjimi-consultants.fr

CS 7643 — Spring 2019 Project

Goal: learn more about the underlying deep learning model of GPT2: the Transformer model and, more broadly, the attention mechanism.

Example showing the use of a pretrained classification network. See also the available tutorials on the SICK support portal. Topics: #algorithm #image-2d #machine-learning #deep-learning #neural-network #sample #sick-appspace

Example showing the use of a pretrained classification network together with an EdgeMatcher to inspect multiple image regions. See also the available tutorials on the SICK support portal. Topics: #algorithm #image-2d #machine-learning #deep-learning #neural-network #sample #sick-appspace #edgematcher #locator #matching

LLM quantization & benchmarking on GPU - GGUF, GPTQ, AWQ, bitsandbytes | Quantification et benchmark de modeles LLM sur GPU

https://ayinedjimi-consultants.fr

An OpenAI Gym for Shopping Cart Reinforcement Learning.

A simplified version of "Cliffworld" in an OpenAI Gym Environment

Biblioteca para adição de mais acessibilidade em páginas da web através de Deep Learning.

IF-Net fork + complete preprocessed ShapeNet archive (tar.xz bundles via git LFS). ShapeNet derivatives: non-commercial research use only.

Joule is a dataset uploader/hoster/downloader.

Joule.Host | Documentation

Projet pédagogique en algorithmie et intelligence artificielle consistant à créer un agent (Kevin) capable de résoudre un labyrinthe.

Le projet explore plusieurs approches :

Génération de labyrinthes (DFS, Prim) Algorithmes de recherche de chemin (A*, Dijkstra) Apprentissage supervisé (Imitation Learning avec CNN) Apprentissage par renforcement (Deep Q-Network)

Des outils de visualisation permettent de générer des images et des GIFs montrant Kevin se déplacer dans le labyrinthe.

Projet réalisé dans le cadre de la formation Développeur en Intelligence Artificielle (Simplon).

Examples demonstrating Kullback-Leibler divergence.

VRAM to RAM Offloader for AI and vLLM - High-Performance C++23 KV Cache Engine with Multi-Stream GPU Transfers

https://ayinedjimi-consultants.fr