luckyrajputana Goto Github PK

aws-sagemaker

backlog-repo

cicdpipelinesmlai

These are the CICID pipeline for the MLAI projects

demo-self-driving

Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.

deploying-machine-learning-models

Example Repo for the Udemy Course "Deployment of Machine Learning Models"

ignite

This is end to end IOT based solutions in which data comes from the RFID sensor in the binary form which is converted to ASCII and retrieve the RFID tag scanned in the sensor to trace the production line bin tracing in the real time scenerio

indian-paper-currency-prediction

Source code of Indian Paper Currency Prediction :fire:

mercedes-benz-greener-manufacturing

This is a regression problem in which i had to predict the optimized testing time of the car on the production line waiting for different types of testing based on the customized features in the car so that company can utilize it's resource well.

mledautility

This repository is created to run the EDA on Dataset.

question_answering_ai

AI2 Reasoning Challenge (ARC) 2018 Multiple-choice science questions The dataset is partitioned into a Challenge Set and an Easy Set Where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm Each question has a multiple choice structure (typically 4 answer options Challenge Set - 2,590 “hard” questions (those that both a retrieval and a co-occurrence method fail to answer correctly) Easy Set of 5,197 questions. Each are pre-split into Train, Development, and Test sets as follows: Challenge Train: 1,119 Challenge Dev: 299 Challenge Test: 1,172 Easy Train: 2,251 Easy Dev: 570 Easy Test: 2,376 Available Dataset - over 14 million science sentences relevant to the task Note that use of the corpus for the Challenge is completely optional, and also that systems are not restricted to this corpus. Please see the README included in the download for additional information and terms of use of this corpus. Available Models - an implementation of three neural baseline models for this dataset.

spmapplication

spmautorepo

spmwebsite

steel-defect-detection

This is a image semantic segmentation problem solutions based on django app and trained on the top of Tensorflow API. The trained model takes an image as an input and predict the class of defect from defect1, defect2, defect3, defect4 or no defect with defected area pixels highlighted.

streamlitapp