Model selection

Anyscale Hosts Ray Summit on Scalable Python and Scalable Machine Learning

Retrieved on: 
Wednesday, February 19, 2020
Networks, Data management, Other Technology, Technology, Software, Artificial intelligence, Learning, Deep learning, Cybernetics, Free statistical software, Model selection, Machine learning, Hyperparameter, Keras, Automated machine learning, Outline of machine learning

Ray Summit brings together a leading community of architects, machine learning engineers, researchers and developers building the next generation of scalable, distributed, high-performance Python and machine learning applications.

Key Points: 
  • Ray Summit brings together a leading community of architects, machine learning engineers, researchers and developers building the next generation of scalable, distributed, high-performance Python and machine learning applications.
  • Experts will showcase Ray best practices, real-world case studies, and the latest research in AI and other scalable systems built on Ray.
  • Topics of discussion will include the basics of using Ray to scale Python applications and machine learning applications; an introduction to Rays popular machine learning libraries including those for reinforcement learning, hyperparameter tuning, model serving, and training; scaling reinforcement learning in practice; and using Keras to classify text with LSTMs and other machine learning techniques.
  • Ray Summit welcomes current Ray users and those who are interested in learning how Ray can meet their needs.

Preferred Networks Releases Optuna v1.0, Open-source Hyperparameter Optimization Framework for Machine Learning

Retrieved on: 
Tuesday, January 14, 2020
Artificial intelligence, Machine learning, Learning, Model selection, Deep learning, Free statistical software, Hyperparameter optimization, Mathematical optimization, Chainer, Hyperparameter, TM

TOKYO, Jan. 14, 2020 /PRNewswire/ -- Preferred Networks, Inc. (PFN, Head Office: Tokyo, President & CEO: Toru Nishikawa) has released Optuna (TM) v1.0, the first major version of the open-source hyperparameter optimization framework for machine learning.

Key Points: 
  • TOKYO, Jan. 14, 2020 /PRNewswire/ -- Preferred Networks, Inc. (PFN, Head Office: Tokyo, President & CEO: Toru Nishikawa) has released Optuna (TM) v1.0, the first major version of the open-source hyperparameter optimization framework for machine learning.
  • PFN will continue to quickly incorporate the results of the latest machine learning research into the development of Optuna and work with the OSS community to promote the use of Optuna.
  • Optuna was open-sourced by PFN in December 2018 as a hyperparameter optimization framework written in Python.
  • *Optuna (TM), Chainer (TM), and MN-Core (TM) are the trademarks or the registered trademarks of Preferred Networks, Inc. in Japan and elsewhere.

Madrona Awards 2019 Madrona Prize to UW Project That Applies Artificial Intelligence to Build Accurate and Easy-to-Use Models for Physicians to Employ in Time-Sensitive and Potentially Life-Saving Situations

Retrieved on: 
Thursday, November 21, 2019
Technology, Finance, Other Technology, Professional services, Other Health, General Health, University, Health, Data management, Education, Articles, Cybernetics, Academic disciplines, Computational neuroscience, Model selection, Science, Futurology, Prediction, Scientific method, Feature selection, Artificial intelligence

CoAI: Cost Aware Artificial Intelligence for Health Care applies ML to help healthcare professionals use accurate predictive models in time-sensitive and potentially life-saving situations.

Key Points: 
  • CoAI: Cost Aware Artificial Intelligence for Health Care applies ML to help healthcare professionals use accurate predictive models in time-sensitive and potentially life-saving situations.
  • The field of cost-sensitive ML builds algorithms that automate the feature selection step, automatically choosing the best subset of input variables to make a high-accuracy prediction.
  • The Madrona Prize is awarded at the end of the Allen Schools annual Industry Affiliates Research Day.
  • Since Madronas inception more than two decades ago, Madrona has funded 18 companies out of the Allen School.

AIC Announces Move To New Headquarters Location

Retrieved on: 
Wednesday, October 2, 2019
Akaike information criterion, Model selection, Scientific modeling, Anime International Company, Scientific method

On September 26, 2019, AIC held a ribbon cutting ceremony, officially opening the new location which has over 15,000 square feet of space and an abundance of natural light.

Key Points: 
  • On September 26, 2019, AIC held a ribbon cutting ceremony, officially opening the new location which has over 15,000 square feet of space and an abundance of natural light.
  • Mr. Birkelbach noted that he and the entire AIC management team and staff are enthusiastic about the move to this new location.
  • AIC is an ISO-Registered specialty chemical and ingredient sales and marketing company serving the North American market since 1972.
  • With annual revenues of over $100 million and approximately 100 employees across North America, AIC is a well-respected and well-established company with significant growth potential.

ArangoDB Extends Open Source Solution with ArangoML Pipeline; First Multi-Model Metadata Layer for Machine Learning Pipelines

Retrieved on: 
Wednesday, October 2, 2019
Data management, Computing, Data, Model selection, Structured storage, Distributed computing architecture, ArangoDB, Project Jupyter, Hyperparameter optimization, Hyperparameter, Machine learning, Automated machine learning

SAN FRANCISCO and COLOGNE, Germany, Oct. 2, 2019 /PRNewswire-PRWeb/ -- ArangoDB , the leading open source native multi-model database, today announced the release of ArangoML Pipeline, the first multi-model metadata layer for Machine Learning (ML) pipelines, an open source project that provides a common metadata layer for production-grade data science and ML platforms.

Key Points: 
  • SAN FRANCISCO and COLOGNE, Germany, Oct. 2, 2019 /PRNewswire-PRWeb/ -- ArangoDB , the leading open source native multi-model database, today announced the release of ArangoML Pipeline, the first multi-model metadata layer for Machine Learning (ML) pipelines, an open source project that provides a common metadata layer for production-grade data science and ML platforms.
  • Machine learning pipelines are all different depending on the project and the company.
  • They contain a number of different components -- from distributed training and Jupyter Notebooks, to hyperparameter optimization and feature stores.
  • With more than 7 million downloads and over 8,000 stargazers on GitHub , ArangoDB is the leading open source native multi-model database.

HQC Inc. Solves Production Challenge for CSM Bakery Solutions

Retrieved on: 
Wednesday, June 20, 2018
Hannan–Quinn information criterion, Model selection, Industrial design, Prototype

To help CSM determine which fitment details would work best with the product, HQC 3D-printed multiple elements and inserted them into the pouches.

Key Points: 
  • To help CSM determine which fitment details would work best with the product, HQC 3D-printed multiple elements and inserted them into the pouches.
  • Once the right elements were approved, HQC built a prototype molding tool and worked further with CSM on finishing modifications.
  • Time-saving efficiency continued as HQC built production tools for the new fitment design while the machinery developer focused on the fully assembled prototype.
  • "The CSM DecoPro project once more demonstrated how HQC guides each customer from concept and expectations to a product that performs in the market," said HQC President Blair Pasternak.