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Scientific topics: Sample collections  or Machine learning 


Machine Learning & BioStatistics Hackathon 2020

Materials created at the Machine Learning and BioStatistics hackathon organised by ELIXIR-GR (CERTH) in October and November 2020.

Scientific topics: Computer science, Statistics and probability, Machine learning

Keywords: machine learning, biostatistics, eLearning, EeLP

Resource type: Training materials

Machine Learning & BioStatistics Hackathon 2020 http://tess.elixir-uk.org/materials/machine-learning-biostatistics-hackathon-2020 Materials created at the Machine Learning and BioStatistics hackathon organised by ELIXIR-GR (CERTH) in October and November 2020. Computer science Statistics and probability Machine learning machine learning, biostatistics, eLearning, EeLP Life Science Researchers statisticians Training Designers Training instructors Trainers
WEBINAR: Getting started with deep learning

This Zenodo record includes training materials associated with the Australian BioCommons webinar ‘Getting started with deep learning’. This webinar took place on 21 July 2021.

Scientific topics: Machine learning

Keywords: Deep learning

WEBINAR: Getting started with deep learning http://tess.elixir-uk.org/materials/webinar-getting-started-with-deep-learning This Zenodo record includes training materials associated with the Australian BioCommons webinar ‘Getting started with deep learning’. This webinar took place on 21 July 2021. Machine learning Deep learning
Deep Learning using a Convolutional Neural Network

This course part focuses on a recent machine learning method known as deep learning that emerged as a promising disruptive approach, allowing knowledge discovery from large datasets in an unprecedented effectiveness and efficiency. It is particularly relevant in research areas, which are not...

Scientific topics: Machine learning

Resource type: Video

Deep Learning using a Convolutional Neural Network http://tess.elixir-uk.org/materials/deep-learning-using-a-convolutional-neural-network This course part focuses on a recent machine learning method known as deep learning that emerged as a promising disruptive approach, allowing knowledge discovery from large datasets in an unprecedented effectiveness and efficiency. It is particularly relevant in research areas, which are not accessible through modelling and simulation often performed in HPC. Traditional learning, which was introduced in the 1950s and became a data-driven paradigm in the 90s, is usually based on an iterative process of feature engineering, learning, and modelling. Although successful on many tasks, the resulting models are often hard to transfer to other datasets and research areas. Machine learning PhD students Post Docs
Introduction to Machine Learning Algorithms

This course offers basics of analysing datasets with machine learning algorithms and data mining techniques in order to understand foundations of learning from large quantities of data.

Scientific topics: Machine learning

Resource type: Video

Introduction to Machine Learning Algorithms http://tess.elixir-uk.org/materials/introduction-to-machine-learning-algorithms-b1434ce7-b934-4b48-af7c-0274e2c37815 This course offers basics of analysing datasets with machine learning algorithms and data mining techniques in order to understand foundations of learning from large quantities of data. Machine learning PhD students Post Docs
Sample prep for Proteomics

Sample prep for proteomics by Monica Schenone.

Scientific topics: Proteomics, Sample collections

Sample prep for Proteomics http://tess.elixir-uk.org/materials/sample-prep-for-proteomics Sample prep for proteomics by Monica Schenone. Proteomics Sample collections