#Programmer
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).
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.
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.
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.
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”.
Google Engineering Practices Documentation
Google has many generalized engineering practices that cover all languages and all projects. These documents represent their collective experience of various best practices that they have developed over time. It is possible that open source projects or other organizations would benefit from this knowledge.