tareqmahin Goto Github PK

fourthyear

getting-and-cleaning-data-coursera

peer review projects of getting and cleaning data on cousera course

github-slideshow

A robot powered training repository :robot:

hand-digit-classification

Classifying handwritten digit with neural network

hub

Dataset format for AI. Build, manage, & visualize datasets for deep learning. Stream data real-time to PyTorch/TensorFlow & version-control it. https://activeloop.ai

identify-question-type

Given a question, the aim is to identify the category it belongs to. The four categories to handle for this assignment are : Who, What, When, Affirmation(yes/no). Label any sentence that does not fall in any of the above four as "Unknown" type.

image-captioning-using-deep-learning-

This notebook is an end-to-end example. When you run the notebook, it downloads the MS-COCO dataset, preprocesses and caches a subset of images using Inception V3, trains an encoder-decoder model, and generates captions on new images using the trained model.

learn_deep_learning_in_6_weeks

This is the Curriculum for "Learn Deep Learning in 6 Weeks" by Siraj Raval on Youtube

machine_learning_journey

This is the Curriculum for "Machine Learning Journey" By Siraj Raval on Youtube

ml-aws-specialty-lab

mlbookcamp-code

The code from the Machine Learning Bookcamp book and a free course based on the book

mobileclassifierdemo

Dogs vs Cats offline Image Classifier built with Azure Custom Vision model exported to Tensorflow

neural-network-mini-project

3 mini projects involving Convolutional Neural Network, Natural Language Processing, Visualization

nlp-task-assignment-tareq-

Identify Question Type: Given a question, the aim is to identify the category it belongs to. The four categories to handle for this assignment are : Who, What, When, Affirmation(yes/no). Label any sentence that does not fall in any of the above four as "Unknown" type.

peer-graded-assignment-course-project-shiny-application-and-reproducible-pitch

This peer assessed assignment has two parts. First, you will create a Shiny application and deploy it on Rstudio's servers. Second, you will use Slidify or Rstudio Presenter to prepare a reproducible pitch presentation about your application. Your Shiny Application Write a shiny application with associated supporting documentation. The documentation should be thought of as whatever a user will need to get started using your application. Deploy the application on Rstudio's shiny server Share the application link by pasting it into the provided text box Share your server.R and ui.R code on github The application must include the following: Some form of input (widget: textbox, radio button, checkbox, ...) Some operation on the ui input in sever.R Some reactive output displayed as a result of server calculations You must also include enough documentation so that a novice user could use your application. The documentation should be at the Shiny website itself. Do not post to an external link. The Shiny application in question is entirely up to you. However, if you're having trouble coming up with ideas, you could start from the simple prediction algorithm done in class and build a new algorithm on one of the R datasets packages. Please make the package simple for the end user, so that they don't need a lot of your prerequisite knowledge to evaluate your application.

peer-graded-assignment-r-markdown-presentation-plotly-tba-

Create a web page presentation using R Markdown that features a plot created with Plotly. Host your webpage on either GitHub Pages, RPubs, or NeoCities. Your webpage must contain the date that you created the document, and it must contain a plot created with Plotly. We would love to see you show off your creativity! Review criterialess The rubric contains the following two questions: Does the web page feature a date and is this date less than two months before the date that you're grading this assignment? Is the web page a presentation and does it feature an interactive plot that appears to have been created with Plotly?

plant-diseases-detector

For this project, we will create an end-to-end Android application with TFLite that will then be open-sourced as a template design pattern. We opte to develop an **Android application that detects plant diseases**.

practical-machine-learning-

Peer review assignment of coursera practical ML course

probability-distribution-

The data analysis and visualization of EA group for the FIFA-19. Different probability distribution have been used to classify the probable outcome.

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera

reproducible-research-project-1--tba-

Reproducible Research Project 1 submission

reproducible-research-week-2-assignmement

segmentation-dataset-with-kmeans-clustering-and-pca

self-driving-car

Udacity Self-Driving Car Engineer Nanodegree projects.

selfie-filter-with-opencv-

This projects illustrate how using ML and DL toolkit we can build an selfie-filter to add on our camera template.

smart-traffic-light-system-using-deep-learning

Integrate a vehicle detection system in a traffic light camera, that could easily track the number of useful things simultaneously.

tableau-business-report-

Create a visualization that provides a breakdown of male and female employees working in the company each year, starting from 1990.

teachablemachine-community

Example code snippets and machine learning code for Teachable Machine

tech-interview-handbook

💯 Curated interview preparation materials for busy engineers

twewy-discord-chatbot

Discord AI Chatbot using DialoGPT, trained on the game transcript of The World Ends With You