Publicación sobre Inteligencia Artificial
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.
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
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.
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.
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.
