The Bible of AI

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R for Data Science

This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science.

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R Notes for Professionals book

This R Notes for Professionals book is compiled from Stack Overflow Documentation. (475 pages, published on May 2018)

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Select Star SQL

This is an interactive book which aims to be the best place on the internet for learning SQL. It is free of charge, free of ads and doesn’t require registration or downloads. It helps you learn by running queries against a real-world dataset to complete projects of consequence. It is not a mere reference page — it conveys a mental model for writing SQL.

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Python® Notes for Professionals book

This Python® Notes for Professionals book is compiled from Stack Overflow Documentation. (816 pages, published on June 2018)

Cognitive Anthropomorphism of AI: How Humans and Computers Classify Images

Modern AI image classifiers have made impressive advances in recent years, but their performance often appears strange or violates expectations of users. This suggests humans engage in cognitive anthropomorphism: expecting AI to have the same nature as human intelligence. This mismatch presents an obstacle to appropriate human-AI interaction.

https://otexts.com/fpp2/

Forecasting: Principles and Practice

A comprehensive introduction to the latest forecasting methods. Examples use R with many data sets taken from the authors’ own consulting experience. In this second edition, all chapters have been updated to cover the latest research, and three new chapters have been added on dynamic regression forecasting, hierarchical forecasting and practical forecasting issues.

https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/

Turing-NLG: A 17-billion-parameter language model by Microsoft

Turing Natural Language Generation (T-NLG) is a 17 billion parameter language model by Microsoft that outperforms the state of the art on many downstream NLP tasks. We present a demo of the model, including its freeform generation, question answering, and summarization capabilities, to academics for feedback and research purposes.

https://arxiv.org/pdf/2001.06309.pdf

Cyber Attack Detection thanks to Machine Learning Algorithms

Cybersecurity attacks are growing both in frequency and sophistication over the years. This increasing sophistication and complexity call for more advancement and continuous innovation in defensive strategies. Traditional methods of intrusion detection and deep packet inspection, while still largely used
and recommended, are no longer sufficient to meet the demands of growing security threats.