AnakinNN's Stars
microsoft/AI-For-Beginners
12 Weeks, 24 Lessons, AI for All!
ProjectPhysX/FluidX3D
The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs via OpenCL. Free for non-commercial use.
opentrack/opentrack
Head tracking software for MS Windows, Linux, and Apple OSX
winpython/winpython
A free Python-distribution for Windows platform, including prebuilt packages for Scientific Python.
JetBrains/swot
Identify email addresses or domains names that belong to colleges or universities. Help automate the process of approving or rejecting academic discounts.
mwaskom/seaborn-data
Data repository for seaborn examples
google/dreambooth
ChawlaAvi/Daily-Dose-of-Data-Science
A collection of code snippets from the publication Daily Dose of Data Science on Substack: http://www.dailydoseofds.com/
dlsucomet/MLResources
Repository for Machine Learning resources, frameworks, and projects. Managed by the DLSU Machine Learning Group.
dask/dask-examples
Easy-to-run example notebooks for Dask
cokelaer/fitter
Fit data to many distributions
Gurobi/gurobi-machinelearning
Formulate trained predictors in Gurobi models
MOSEK/Tutorials
A collection of tutorials for the MOSEK package
BioDynaMo/biodynamo
BioDynaMo is a high-performance and modular, agent-based simulation platform.
OptMLGroup/DeepBeerInventory-RL
The code for the SRDQN algorithm to train an agent for the beer game problem
brentertainer/pyomo-tutorials
mcnakhaee/palmerpenguins
Easily load the Palmer Penguins dataset in python
ReliaQualAssociates/ramstk
Reliability, Availability, Maintainability, Safety (RAMS) analysis program.
rahul-raoniar/Rahul_Raoniar_Blogs
Contains code and data for "The Researchers' Guide" Medium blogs
tstaunton/Learn-Data-Science-with-Python
prasertcbs/scikitlearn_tutorial
สอน machine learning ด้วย scikit-learn
LinkedInLearning/using-large-datasets-with-pandas-4467955
This repo is for linkedin learning course: Using Large Datasets with pandas
dataclergy/Simulation-and-Optimization-with-Python
Part 1: Simulation Create a simulation for the London-Birmingham section of the high-speed line under assumption of a number k of signalling blocks. A decision on the number of signaling blocks is required, as the track laying is supposed to start soon. Also a decision on the number of trains running per hour has to be made. Depending on the number of signalling blocks simulate the throughput of trains. Take into account that between Birmingham Interchange and Birmingham Curzon street as well as between London Old Oak Common and London Euston there will be a practical speed limit and the traveling times are 5 min between London Euston and London Old Oak Common (one signalling block) and 9 min between Birhigham Interchange and Birmingham Curzon Street station (two signalling blocks). Take into account the variability of achievable speeds due to wind and weather conditions on the stretch between London Old Oak Common and Birmingham Interchange. Take into account the variability in stop times, i.e. minor delays due to passenger movements. London Euston 5 min London Old Oak Common 31 min Birmingham Interchange 9 min Birmingham Curzon Street The distance between London Old Oak Common Station and Birmingham Interchange Station is 145km. This could be broken down in up-to 14 blocks. If there are less but longer blocks the trains could achieve in theory a higher average speed, however the throughput in trains per hour is smaller. Part 2: Optimisation For the project you need to create a simulation of the train network which would give for a given train schedule (numbers of trains per hour n ∈ {1, 2, 3, 4, 5, 6, 8, 10, 12, 15, 20}) and a given break-down of the line in signalling blocks k ∈ {1, ..., 15} a distribution of average overall travelling time. The simulation results are fed into an optimisation problem to determine (nopt, kopt) to achieve an optimal results. Using the simulation described above solve the following optimisation problems: (1) Minimise the overall average traveling time. The overall traveling time consist of the waiting time for the next train and the actual traveling time until arrival of the train. The problem can be simplified by assuming a fixed average waiting time (0.5*60/n). (2) Maximise the throughput of passengers in peak hours. The trains can be configured to run at a length of 200m (short train, 420 passengers) or 300m (max train, 630 passengers). While longer trains carry potentially more passengers, you need to consider the walking times at the train station. The walking speed of a train passenger carrying luggage is between 1.0 and 1.2m/s, which impacts on the stop times at the station. Also more passengers disembarking and embarking may lengthen the stop times. For a more realistic simulation assume a Poisson-Distribution for passengers arriving at the train station at an average rate of m passengers per hour and how they can be served by n trains per hour. As the trains are on a tight schedule, we cannot assume that everyone gets on board during the short stopping time. Assume that the trains are only filled to 70% of nominal capacity. A backlog of passengers on the departing station will impact on the average travelling time.
tstaunton/python-projects
APTD/Simulation
Asimov - Agentbased Passenger Train Delay
76ezgames/76ezgames.github.io
Play the best unblocked games you can find online with friends in school or anywhere. https://76ezgames.com - Most comprehensive list you can find online available at top 1gbps speed.
kjy/Optimizing_Public_Transportation
Kafka producer (train arrival and turnstile event for passenger arrival), Kafka Rest Proxy producer for weather data, Kafka Connect for JDBC source connector to extract data from Postgres, Faust Stream Processor, KSQL table, Kafka consumer, run simulation with Docker-compose environment
stevendae/TrainSimulation
vibbix/TrainSimulation
A simulation of a train route
yuniyakim/web-scraping
Web scraper for articles at Google Scholar website