A database housing more than 100 Colab notebooks running ML code for various NLP tasks. Colab is an excellent destination to experiment with the latest models as it comes with a free GPU/TPU housed in Google’s back-end servers… And a collection of more than 400 NLP datasets that it include papers.
«ENISA CSIRT training material was introduced in 2008. In 2012, 2013 and 2014 it was complemented with new exercise scenarios containing essential material for success in the CSIRT community and in the field of information security. In these pages you will find the ENISA CSIRT training material, containing Handbooks for teachers, Toolsets for students and Virtual Images to support hands on training sessions. » The materials continue to be updated in 2020 and are appropriate for use by cybersecurity specialists and decision-makers.
(Change of dates to 2021).Top cyber talents from each participating country will meet in Vienna to network and collaborate and finally compete against each other. Contestants will be challenged in solving security related tasks from domains such as web security, mobile security, crypto puzzles, reverse engineering and forensics and in the process collect points for solving them.
By the end of this you will have had a whirlwind tour of the very tip of the data visualization best-practices iceberg. We will go over a broad range of topics generally applicable to data science usecases but not dive too deep into any single one. One thing to keep in mind the whole time is none of this is absolutely set in stone, most often in the real world you have to bend or break some of these rules to do what you want.
This lecture will outline the fundamental ML processes involved in medical image analysis. Achieving prediction and classification for CAD applications will also be discussed. Some preliminary ideas of 3D reconstruction and viewing as applied in medical image analysis will also be presented.
The objective of this guideline is to provide medical device manufacturers and notified bodies instructions and to provide them with a concrete checklist to understand what the expectations of the notified bodies are, to promote step-by-step implementation of safety of medical devices, that implement artificial intelligence methods, in particular machine learning, to compensate for the lack of a harmonized standard (in the interim) to the greatest extent possible.
