pdwytr Goto Github PK

aws-amplify-quick-notes

bootstrap_sample_one

databasestar

All of the SQL scripts used on my website.

dbconnect

forecasting-sales-of-walmart-retail-goods

Note: This is one of the two complementary competitions that together comprise the M5 forecasting challenge. Can you estimate, as precisely as possible, the point forecasts of the unit sales of various products sold in the USA by Walmart? If you are interested in estimating the uncertainty distribution of the realized values of the same series, be sure to check out its companion competition How much camping gear will one store sell each month in a year? To the uninitiated, calculating sales at this level may seem as difficult as predicting the weather. Both types of forecasting rely on science and historical data. While a wrong weather forecast may result in you carrying around an umbrella on a sunny day, inaccurate business forecasts could result in actual or opportunity losses. In this competition, in addition to traditional forecasting methods you’re also challenged to use machine learning to improve forecast accuracy. The Makridakis Open Forecasting Center (MOFC) at the University of Nicosia conducts cutting-edge forecasting research and provides business forecast training. It helps companies achieve accurate predictions, estimate the levels of uncertainty, avoiding costly mistakes, and apply best forecasting practices. The MOFC is well known for its Makridakis Competitions, the first of which ran in the 1980s. In this competition, the fifth iteration, you will use hierarchical sales data from Walmart, the world’s largest company by revenue, to forecast daily sales for the next 28 days. The data, covers stores in three US States (California, Texas, and Wisconsin) and includes item level, department, product categories, and store details. In addition, it has explanatory variables such as price, promotions, day of the week, and special events. Together, this robust dataset can be used to improve forecasting accuracy. If successful, your work will continue to advance the theory and practice of forecasting. The methods used can be applied in various business areas, such as setting up appropriate inventory or service levels. Through its business support and training, the MOFC will help distribute the tools and knowledge so others can achieve more accurate and better calibrated forecasts, reduce waste and be able to appreciate uncertainty and its risk implications. Acknowledgements Additional thanks go to other partner organizations and prize sponsors, National Technical University of Athens (NTUA), INSEAD, Google, Uber and IIF.

frauddetection

github-slideshow

A robot powered training repository :robot:

linkedinjobscraper

Give Job title and Loaction, get valuable job insights for both job seekers and providers

muftibot

mysite

This is content for my website

optimizing-windmill-layout-competition-by-shell-ltd

Shell.ai Hackathon for Sustainable and Affordable Energy The world needs to move to a cleaner energy system if it is to meet growing energy demand while tackling climate change. In April 2020, Shell shared its ambition to become a net-zero emissions energy business by 2050, or sooner. Renewable electricity is central to this ambition. Electricity is the fastest-growing part of the energy system and, when generated from renewable sources such as wind, has a big role to play in reducing greenhouse gas emissions. We see digitalisation and AI as key enablers to the energy transition. Challenge: Windfarm Layout Optimisation In this Shell.ai Hackathon for Sustainable and Affordable Energy, we invite you to optimise the placement of 50 wind turbines of 100 m height and100 m rotor diameter each on a hypothetical 2D offshore wind farm area such that the AEP (Annual Energy Production) of the farm is maximized. One of the key problems of an unoptimized layout is the combined effect wind turbines can have on the wind speed distribution in a windfarm. As a wind turbine extracts energy from incoming wind, it creates a region behind it downstream where the wind speed is decreased- this is called a wake region. Note that wind turbines automatically orient their rotors, to face incoming wind from any direction. Due to the induced speed deficit, a turbine placed inside the wake region of an upstream turbine will naturally generate reduced electrical power. This inter-turbine interference is known as a wake effect. An optimal windfarm layout is important to ensure a minimum loss of power during this combined wake effect. This Shell.ai Hackathon for Sustainable and Affordable Energy edition, focuses on an interesting and complex coding problem. When competing, you will face challenges such as a high dimensionality, complex multimodality and the discontinuous nature of the search space. This makes optimizing the layout analytics difficult. But, armed with optimization strategies and computer algorithms, you can solve this problem.

pdwytr.github.io

This course about developing high grade dynamic APIs using fastapi which can be tweaked easily and made ready for prodcution

recognising-the-specific-activities-from-video

Now a day’s recognizing of the activity from the surveillance video is a challenging task. Some activities are not a regular human activity, sometimes it belongs to sports activity or other activities. The sports Activity is the Collection of action /activity e.g. High jump, cricket bowling. There are different types of human activity which are categories in three parts. First, single activity: repeating the same action in the loop. Second, Chain of single Activity: the combination of different Activities of the first type and Third, Activity interaction with object or person. Here focused on the second type of activity mostly found in sports and highlight these activities present video.

scipy

SciPy library main repository

submission-repository