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Name: Edgar Bahilo Rodríguez
Type: User
Bio: Towards AI-driven industry
Location: Sweden
Name: Edgar Bahilo Rodríguez
Type: User
Bio: Towards AI-driven industry
Location: Sweden
Auto encoder for time series
Este tutorial muestra como integrar algoritmos propios a Amazon Sagemaker.
Distributed Tensorflow, Keras, PyTorch and BigDL on Apache Spark
Anomaly detection related books, papers, videos, and toolboxes
Processes the input and output data of ANTARES
ANTARES Visualizations
Australia's goals of increased penetration of renewable energy such as wind energy will inevitably lead to increased variability and uncertainty of the ramps in net load (load minus non-dispatchable renewable generation). This increased variability and uncertainty requires conventional generators to be more flexible, but currently this flexibility is not fully integrated in market processes. The provision of additional flexibility may cause a reduction in economic efficiency, consumer surplus and/or producer surplus as conventional generators may need to modify their output from the optimal level in order to provide flexibility to account for future variability and uncertainty. As a solution to this problem, the Midwest and Californian Independent System Operators have proposed flexible ramping products as a mechanism to manage the uncertainty and variability in net load ramps in an economically preferable manner. The mechanism essentially aims to schedule conventional generators to provide enough ramping capability, or "flexibility", to satisfy a flexible ramping capability requirement. This requirement is designed to ensure a certain range of ramps in the next interval could be met, whether the ramps actually occur or not. This study aims to explore the implementation of flexible ramping products in the specific context of the Australian National Electricity Market (NEM), to determine whether or not they can be an effective mechanism for integrating variable renewable energy in Australia in the coming decades. This model is a simplified model of the Australian NEM, in which a unit commitment and economic dispatch is designed with flexible ramping products and a flexible ramping requirement. The simplification of the NEM includes a grouping of the five states into two regions, and an aggregation of generators by offered ramping speed and actual marginal costs. Actual load and wind generation data from the 2014/15 financial year is implemented in the model to attempt to simulate the market in a realistic manner.
A bunch of data sets to evaluate autoML tooling (autogluon, pycaret, h2o, cloud services for autoML etc)
Autoencoder model for rare event classification
Deploy AutoML as a service using Flask
A curated list of awesome data labeling tools
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
This curated list contains python packages for time series analysis
Experiments to run R in AWS Lambda
Tools for computation on batch systems
Content (slide decks etc) from presentations related to use of R (#rstats) in the B.C. government
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
A demo of an end-to-end machine learning pipeline, using RStudio Connect
Collection of the codes which are used on our blog at https://www.statworx.com/de/blog/
A Bokeh project developed for learning and teaching Bokeh interactive plotting!
LSTM Model for Electric Load Forecasting
This project challenging and interesting enough for me. I open to you who wanna join in this project. for having fun guys. money is bonus.
A Curated list of R uses in entreprise
This repo contains some tutorial type programs showing some basic ways machine learning can be applied to CFD.
Energy industry solutions using the Cortana Intelligence Suite with end-to-end walkthrough.
Assignments for Coursera DataScience Specialization
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.