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CARLA (Counterfactual And Recourse LibrAry), a python library for benchmarking counterfactual explanation methods across both different data sets and different machine learning models. In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of these methods. We have open-sourced CARLA and our experimental results on Github, making them available as competitive baselines. We welcome contributions from other research groups and practitioners.
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations, and the website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.
EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions, it is a library for automated machine learning (AutoML) and model understanding, written in Python
We summarize current datasets and metrics for evaluating GNN explainability. Altogether, this work provides a unified methodological treatment of GNN explainability and a standardized testbed for evaluations.
In this short book, we illustrate some of the core algorithms/functions of this popular Python library for image processing and manipulation tasks, with hands-on code examples.
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
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).
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.
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.
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.
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.
“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”.
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
An extensive list of fundamental machine learning models and algorithms from scratch in vanilla Python.
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.
The SciPy library, accompanied by its interdependent NumPy, offers Python programmers advanced functions that work with arrays and matrices. Each section presents a complete demo program for programmers to experiment with, carefully chosen examples to best illustrate each function, and resources for further learning. Use this e-book to install and edit SciPy, and use arrays, matrices, and combinatorics in Python programming.
C Notes for Professionals book is compiled from Stack Overflow Documentation. (333 pages, published on May 2018)
Toolbox for the use of technology and data to combat COVID-19: mobile applications and the use of anonymised mobility data
This recommendation sets up a process for developing a common approach, referred to as a Toolbox, to use digital means to address the crisis. The Toolbox will consist of practical measures for making effective use of technologies and data, with a focus on two areas
iOS® Developer Notes for Professionals book is compiled from Stack Overflow Documentation. (874 pages, published on May 2018)
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Android™ Notes for Professionals book is compiled from Stack Overflow Documentation. (1301 pages, published on May 2018)
El diseño del kit de herramientas permite trabajar en paralelo entre más de 70 idiomas, utilizando el formalismo de Dependencias Universales. Stanza está construido con componentes de red neuronal de alta precisión, que también permiten una capacitación y evaluación eficientes con sus propios datos anotados.
In Implementing a Custom Language Succinctly, Succinctly series author Vassili Kaplan demonstrated how to create a customized programming language. Now, he returns to showcase how you can use that language to build fully functional mobile apps. In Writing Native Mobile Apps in a Functional Language Succinctly, you will build off the skills you’ve already developed to begin creating applications that you can put to immediate use.
Machine learning uses tools from a variety of mathematical fields. This document is an attempt to provide a summary of the mathematical background needed.
Chris Rose guides readers through the basics of Scala, from installation to syntax shorthand, so that they can get up and running quickly.
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