artimus07's Stars
yuvraaj2002/InterviewX
Welcome to InterviewX, your ultimate interview companion powered by AI. With features like scam detection, resume optimization, real-time posture analysis, time-saving text summarization, and ice breaker insights, InterviewX equips you with everything you need to ace your next interview with confidence and ease.
niall-turbitt/e2e-mlops
[DEPRECATED] Demo repository implementing an end-to-end MLOps workflow on Databricks. Project derived from dbx basic python template
N00Bception/AI-Powered-5G-OpenRAN-Optimizer
This advanced and complex project implements an AI-powered optimization system for 5G Open RAN networks. Using machine learning and deep learning, the system optimizes network performance by detecting anomalies, predicting network traffic, and dynamically allocating resources.
cavallimarko/WolfTracksTessellation
This project showcases tessellation-based snow tracks made in the Unity game engine. The main character is a wolf with a walk, run and idle animations. The user controls the movement of the wolf and the camera rotation. The scene contains snowy mountain environment with fog and a snow particle system. Every polygon in plane on which wolf moves is tessellated based on the distance from the camera. Every footstep is accompanied by a snow track on that spot, color change and a sound. Over time snow accumulates so that the tracks disappear slowly.
lacatus/TFM
This project contains the code related to my Master Thesis in Computer Vision. The aim of the project is to make a Real Time people tracking system for Surveillance purposes. The main software used for this project is the Open Source Computer Vision library, OpenCV, running under Python. Also for optimization purposes, Cython is used.
AsoStrife/Computer-Vision-Project
The goal of this project was to develop a Face Recognition application using a Local Binary Pattern approach and, using the same approach, develop a real time Face Recognition application.
sumeghanglekar/Microservices-Based-Real-Time-Sentiment-Analysis-of-YouTube-Livechat-using-NLP
A microservices-based application that studies the sentiment of YouTube live chat comments in real-time using Python, YouTube API, Docker containers and Kafka streaming service.
ayushoriginal/Sentiment-Analysis-Twitter
:mortar_board:RESEARCH [NLP :thought_balloon:] We use different feature sets and machine learning classifiers to determine the best combination for sentiment analysis of twitter.
facebookresearch/dlrm
An implementation of a deep learning recommendation model (DLRM)
Woodman718/FixCaps
FixCaps: An Improved Capsules Network for Diagnosis of Skin Cancer，DOI: 10.1109/ACCESS.2022.3181225
google/project-gameface
praneeth-katuri/PhishShield
PhishShield is an open-source project aimed at detecting phishing websites using machine learning techniques. Leveraging advanced algorithms, PhishShield analyzes various features of URLs to distinguish between legitimate websites and potential phishing attempts.
DevOpsThinh/Coding-Deep-Learning
A self-taught project about Machine Learning & Deep Learning with Python. Thanks to the countless researchers and developers around the world and their open-source code, particularly Python-based open-source code!
neorusse/sklearn-k8s
The project goal is to operationalize a pre-built SKLearn Machine Learning Microservice Application using Kubernetes, which is an open-source system for automating the management of containerized applications.
mthd98/Project-Algorithm-for-a-Dog-Identification-App
Project Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.
ashish2812tiwari/Mchine-learning---select-best-regression-model-sales-data-
To choose best ML regression model for sales data
VikalpVijay/End-to-End-Steering-Control-Mechanism
The project self-driving (autonomous) vehicle, uses Udacity open-source simulator to implement deep-reinforcement and semi-supervised learning, The provided simulator is the sole property of Udacity.
Harrison1/unrealcpp
Unreal Engine 4 C++ examples
calebcram/Crossplatform-Multiplayer-Rover-AR-Demo
This is the NASA Perseverance rover in an Augmented Reality experience hosted in the web. This was built in Unity with Photon Pun 2 for the multiplayer networking and MRTK for the interactions on various device platforms and WebXR /WebGL to convert the project into a format compatible with the web.