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LASER-UMASS / SBIR ReplicationPackageThis repository contains the source code, data, and results described in the paper titled: Better Automatic Program Repair by Using Bug Reports and Tests Together, in Proceedings of the 45th International Conference on Software Engineering (ICSE), 2023 by Manish Motwani and Yuriy Brun.
mthd98 / Project Algorithm For A Dog Identification AppProject 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.
beratturan / Real Time E Commerce Recommendation AppThis is a software engineering project for an e-commerce platform to build batch and real-time data pipelines together with REST APIs to create a real-time recommendation engine. The REST API will be the source of two recommendation lists on the main page: browsing history and best seller products.
shazaaly / Technical Interview PrepPrepare to ace your technical interviews with our focused and collaborative study group. Whether you're aiming for a role in software engineering, data science, or any tech-driven field, our group offers a structured pathway to sharpen your problem-solving skills, coding proficiency, and understanding of key concepts.
feiwww / SNS Features DatasetSoftware measure datasets of software network structure for defect prediction
spec-rgdevops / OPEN.xtraceOpen Execution Trace Exchange (OPEN.xtrace) is a format that enables data interoperability and exchange between APM tools and software performance engineering (SPE) approaches.
sola-st / Llm Agents StudyCode and data for study: "Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories"
Dinghow / DingdongBookTongji University, School of Software Engineering, Database Course Design (42028901). An online book store website, the project of data base course design
rohanmistry231 / Tech World RoadmapsYour ultimate guide to mastering tech in 2025! This repo offers structured roadmaps for AI/ML, Blockchain, Cloud Computing, Cyber Security, Data Science, DevOps/SRE, Game Development, IoT, Quantum Computing, and Software Engineering.
PkLavc / PkLavc.github.ioOfficial Professional Portfolio & Engineering Showcase. A centralized platform featuring my career trajectory as a Software Engineer, showcasing expertise in backend systems, system integrations, and resilient data pipelines. Designed with modern web standards and high-performance architecture.
nagarajulu / Inteview Prep NotesWIP.. My quick reference notes for software engineering interviews- data structures, and algorithms
aiforse / Artificial Intelligence For Software Engineering Consolidated InformationExisting Solutions, Libraries, Data Sources and Statistics on applying Artificial Intelligence for Software Engineering
WM-SEMERU / Ds4seData Science for Software Engineering (ds4se) is an academic initiative to perform exploratory and causal inference analysis on software engineering artifacts and metadata. Data Management, Analysis, and Benchmarking for DL and Traceability.
nasa / ML Airport Data ServicesThe ML-airport-data-services software is developed to provide common code used throughout the ML-airport suite of software. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides useful functions for development of pipelines including data query and save, data engineering, and data science.
dimitrio25 / SoftEngSSCSoftware Engineering Data Files of BSIT 404
nasa / ML Airport Arrival RunwayThe ML-airport-arrival-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting arrival runway assignments. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
nasa / ML Airport Departure RunwayThe ML-airport-departure-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting departure runway assignments. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.
agile-lab-dev / Literate Programming ArticlesCollection of articles, using the Literate Programming style, about Data Engineering and Software Tooling in general
mridha-rakib / Mridha RakibI am a final-year student in Software Engineering background. Skilled in algorithms, data structures, problem-solving, and programming languages. Also, I have the patience to work in the field of cyber security. I'm always excited about learning new things and competitive skills.
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