Candida/Di's repositories
Accessible-Interactives
DIAGRAM Center's Accessible Interactive's Library
BMW-TensorFlow-Training-GUI
This repository allows you to get started with a gui based training a State-of-the-art Deep Learning model with little to no configuration needed! Training with TensorFlow has never been so easy.
dataloader
apex data loader
DevOps-Apprenticeship-Notes
Notes and documentation for our DevOps Apprentices
firebase-android-mobile-app-photo-upload
A sample firebase driven mobile app for uploading and displaying images with simple authentication system.
geth-poa-tutorial
Clique Proof-of-Authority Tutorial
go-ethereum
Official Go implementation of the Ethereum protocol
hack-the-media
This repo collects examples of intentional and unintentional hacks of media sources
ImageUploader
Mobile App to Load Image from Gallery
linkedin_scraper
A library that scrapes Linkedin for user data
maple-file
upload and storage images server in python.
MobileNet_tfjs-node_Server
This is a mobile app using MobileNet model with tfjs-node to predict objects from an image on a server side. Image upload done with Multer a node.js middleware. Frontend with reactjs. Live demo on: https://safe-woodland-21690.herokuapp.com
multisig-wallet-exercise
Let's start creating a multisig wallet on Ethereum.
openzeppelin-contracts
OpenZeppelin Contracts is a library for secure smart contract development.
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.
Real-Time-Voice-Cloning
Clone a voice in 5 seconds to generate arbitrary speech in real-time
scrape-linkedin-selenium
`scrape_linkedin` is a python package that allows you to scrape personal LinkedIn profiles & company pages - turning the data into structured json.
seaborn
Statistical data visualization in Python
simple-bank-exercise-TheLady
simple-bank-exercise-TheLady created by GitHub Classroom
uniswap-v2-core
🎛 Core smart contracts of Uniswap V2
web3-react
🧰 A simple, maximally extensible, dependency minimized framework for building modern Ethereum dApps