I wrote this book because: • ML is not a recipe. It is not a matter of knowing the syntax and mechanics of various software packages.• ML is an art, not a science. (Hence the title of this book). • One does not have to be a math whiz or know advanced math in orer to use ML effectively, but one does need to understand the concepts well — the Why? and How? of ML methods
The Art of Machine Learning (Algorithms + Data + R) Leer más »
This repository contains examples and best practices for building NLP systems, provided as Jupyter notebooks and utility functions. The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language
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.
The Super Duper NLP Repo & The Big Bad NLP Database Leer más »
Harnessing the power of supercomputer and patient modelling to deliver unparallelled medical insights and predict treatment outcomes for patients.
We survey 146 papers analyzing «bias» in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing «bias» is an inherently normative process.
Language (Technology) is Power: A Critical Survey of «Bias» in NLP Leer más »
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.
Machine Learning for Medical Imaging Analysis Demystified Leer más »
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.
Technologies are not neutral, neither are choices in the public procurement of AI. The AI systems we deploy today are the systems we will live with tomorrow.
Data Ethics in Public Procurement of AI-based Services and Solutions Leer más »
This paper contributes the first human-centered observational study of a deep learning system deployed directly in clinical care with patients. Through field observations and interviews at eleven clinics across Thailand, we explored the expectations and realities that nurses encounter in bringing a deep learning model into their clinical practices. First, we outline typical eye-screening workflows and challenges that nurses experience when screening hundreds of patients. Then, we explore the expectations nurses have for an AI-assisted eye screening process. Next, we present a human-centered, observational study of the deep learning system used in clinical care, examining nurses’ experiences with the system, and the socio-environmental factors that impacted system performance. Finally, we conclude with a discussion around applications of HCI methods to the evaluation of deep learning algorithms in clinical environments.
The aim is to give a general overview of different legal regimes having an impact on the planning, development and deployment of Artificial Intelligence systems.
Legal boundaries and AI: an overview of the ecosystem Confirmation Leer más »