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Uncodedtech / repository
This repository includes links to forked repositories which hold a list of resources that could be helpful for a person to ace his/her next job interview!
This repository includes links to forked repositories which hold a list of resources that could be helpful for a person to ace his/her next job interview!
Selected from shared topics, language and repository description—not editorial ratings.
FazeelUsmani /
This repository includes all the homework, assignment and contest solutions taught at Scaler Academy
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.
iamumardevx /
This repository contains daily advanced backend exercises aimed at improving Python skills and deepening understanding of backend development concepts. It includes a variety of tasks focused on problem-solving, algorithmic thinking, and building efficient, scalable solutions
AryamanTewari /
FlyAway (An Airline Booking Portal). Project 2 DESCRIPTION Project objective: As a Full Stack Developer, design and develop an airline booking portal named as FlyAway. Use the GitHub repository to manage the project artifacts. Background of the problem statement: FlyAway is a ticket-booking portal that lets people book flights on their website. The website needs to have the following features: ● A search form in the homepage to allow entry of travel details, like the date of travel, source, destination, and the number of persons. ● Based on the travel details entered, it will show the available flights with their ticket prices. ● Once a person selects a flight to book, they will be taken to a register page where they must fill in their personal details. In the next page, they are shown the flight details of the flight that they are booking, and the payment is done via a dummy payment gateway. On completion of the payment, they are shown a confirmation page with the details of the booking. For the above features to work, there will be an admin backend with the following features: ● An admin login page where the admin can change the password after login, if he wishes ● A master list of places for source and destination ● A master list of airlines ● A list of flights where each flight has a source, destination, airline, and ticket price The goal of the company is to deliver a high-end quality product as early as possible. The flow and features of the application: ● Plan more than two sprints to complete the application ● Document the flow of the application and prepare a flow chart ● List the core concepts and algorithms being used to complete this application ● Implement the appropriate concepts, such as exceptions, collections, and sorting techniques for source code optimization and increased performance You must use the following: ● Eclipse/IntelliJ: An IDE to code for the application ● Java: A programming language to develop the web pages, databases, and others ● SQL: To create tables for admin, airlines, and other specifics ● Maven: To create a web-enabled Maven project ● Git: To connect and push files from the local system to GitHub ● GitHub: To store the application code and track its versions ● Scrum: An efficient agile framework to deliver the product incrementally ● Search and Sort techniques: Data structures used for the project ● Specification document: Any open-source document or Google Docs The following requirements should be met: ● The source code should be pushed to your GitHub repository. You need to document the steps and write the algorithms in it. ● The submission of your GitHub repository link is mandatory. In order to track your task, you need to share the link of the repository. You can add a section in your document. ● Document the step-by-step process starting from sprint planning to the product release. ● The application should not close, exit, or throw an exception if the user specifies an invalid input. ● You need to submit the final specification document which will include: ● Project and developer details ● Sprints planned and the tasks achieved in them ● Algorithms and flowcharts of the application ● Core concepts used in the project ● Links to the GitHub repository to verify the project completion
ApalaSandeepReddy /
DESCRIPTION Project objective: As a Full Stack Developer, design and develop a backend administrative portal for the Learner’s Academy. Use the GitHub repository to manage the project artifacts. Background of the problem statement: Learner’s Academy is a school that has an online management system. The system keeps track of its classes, subjects, students, and teachers. It has a back-office application with a single administrator login. The administrator can: ● Set up a master list of all the subjects for all the classes ● Set up a master list of all the teachers ● Set up a master list of all the classes ● Assign classes for subjects from the master list ● Assign teachers to a class for a subject (A teacher can be assigned to different classes for different subjects) ● Get a master list of students (Each student must be assigned to a single class) There will be an option to view a Class Report which will show all the information about the class, such as the list of students, subjects, and teachers The goal of the company is to deliver a high-end quality product as early as possible. The flow and features of the application: ● Plan more than two sprints to complete the application ● Document the flow of the application and prepare a flow chart ● List the core concepts and algorithms being used to complete this application ● Implement the appropriate concepts, such as exceptions, collections, and sorting techniques for source code optimization and increased performance You must use the following: ● Eclipse/IntelliJ: An IDE to code for the application ● Java: A programming language to develop the web pages, databases, and others ● SQL: To create tables for admin, classes, students, and other specifics ● Git: To connect and push files from the local system to GitHub ● GitHub: To store the application code and track its versions ● Scrum: An efficient agile framework to deliver the product incrementally ● Search and Sort techniques: Data structures used for the project ● Specification document: Any open-source document or Google Docs The following requirements should be met: ● The source code should be pushed to your GitHub repository. You need to document the steps and write the algorithms in it. ● The submission of your GitHub repository link is mandatory. In order to track your task, you need to share the link of the repository. You can add a section in your document. ● Document the process step-by-step starting from sprint planning to the product release. ● The application should not close, exit, or throw an exception if the user specifies an invalid input. ● You need to submit the final specification document which will include: ● Project and developer details ● Sprints planned and the tasks achieved in them ● Algorithms and flowcharts of the application ● Core concepts used in the project ● Links to the GitHub repository to verify the project completion
shared-processing-unit /
Processing Units are actors usually provided by websites. The repository contains the code that is executed on the SPU. This includes the algorithms and the communication layer with the backend.