#Programmer

https://editorialia.com/wp-content/uploads/2020/06/openmined-opensource-to-make-privacy-preserving-of-ai-technologies.jpg

OpenMined: open source to make privacy-preserving of AI technologies

With OpenMined, an AI model can be governed by multiple owners and trained securely on an unseen, distributed dataset.The mission of the OpenMined community is to create an accessible ecosystem of tools for private, secure, multi-owner governed AI

https://editorialia.com/wp-content/uploads/2020/06/undergraduate-diagnostic-imaging-fundamentals.jpg

Undergraduate Diagnostic Imaging Fundamentals

The structure and content of this work has been guided by the curricula developed by the European Society of Radiology, the Royal College of Radiologists, the Alliance of Medical Student Educators in Radiology, with guidance and input from Canadian Radiology Undergraduate Education Coordinators, and the Canadian Heads of Academic Radiology (CHAR).

https://editorialia.com/wp-content/uploads/2020/06/toolkit-for-healthcare-imaging.jpg

Medical Open Network for AI (MONAI), AI Toolkit for Healthcare Imaging

The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.

https://editorialia.com/wp-content/uploads/2020/06/privacy-preserving-ai.jpg

Privacy Preserving AI – Andrew Trask, OpenMined

Learn the basics of secure and private AI techniques, including federated learning and secure multi-party computation. In this talk, Andrew Trask of OpenMined highlights the importance of privacy preserving machine learning, and how to use privacy-focused tools like PySyft.

https://editorialia.com/wp-content/uploads/2020/06/cover-interpretable-machine-learning-1.jpg

Interpretable Machine Learning (A Guide for Making Black Box Models Explainable)

The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.

https://editorialia.com/wp-content/uploads/2020/06/unconventional-computer-arithmetic-for-emerging-applications-and-technologies.jpg

Unconventional Computer Arithmetic for Emerging Applications and Technologies

Arithmetic plays a major role in computing performance and efficiency. It is challenging to build platforms, ranging from embedded devices to high performance computers, supported on traditional binary arithmetic and silicon-based technologies that meet the requirements of today’s applications. In this talk, the state-of-the-art of non-conventional computer arithmetic is presented, considering alternative computing models and emerging technologies.

https://editorialia.com/wp-content/uploads/2020/06/dive-into-deep-learning.jpg

Dive into Deep Learning

“We set out to create a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large; and (v) be complemented by a forum for interactive discussion of technical details and to answer questions”.

https://editorialia.com/wp-content/uploads/2020/06/composing-ai-pipelines-with-ai4eu-experiments.jpg

Composing AI Pipelines with AI4EU Experiments

Show how to onboard AI tools as re-usable building blocks that then can be used to easily compose AI pipelines in the AI4EU Experiments visual editor

https://editorialia.com/wp-content/uploads/2020/05/machine_learning_from_scratch-2.jpg

Machine Learning From Scratch

An extensive list of fundamental machine learning models and algorithms from scratch in vanilla Python.