If you are interested in learning Data Science with R, but not interested in spending money on books, you are definitely in a very good space. There are a number of fantastic R/Data Science books and resources available online for free from top most creators and scientists. Here are such 13 free 20 free (so […]
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
This book discusses the necessity and perhaps urgency for the regulation of algorithms on which new technologies rely; technologies that have the potential to re-shape human societies. From commerce and farming to medical care and education, it is difficult to find any aspect of our lives that will not be affected by these emerging technologies. At the same time, artificial intelligence, deep learning, machine learning, cognitive computing, blockchain, virtual reality and augmented reality, belong to the fields most likely to affect law and, in particular, administrative law. The book examines universally applicable patterns in administrative decisions and judicial rulings. First, similarities and divergence in behavior among the different cases are identified by analyzing parameters ranging from geographical location and administrative decisions to judicial reasoning and legal basis. As it turns out, in several of the cases presented, sources of general law, such as competition or labor law, are invoked as a legal basis, due to the lack of current specialized legislation. This book also investigates the role and significance of national and indeed supranational regulatory bodies for advanced algorithms and considers ENISA, an EU agency that focuses on network and information security, as an interesting candidate for a European regulator of advanced algorithms. Lastly, it discusses the involvement of representative institutions in algorithmic regulation.
Feature engineering plays a vital role in big data analytics. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features.
If you are interested in learning R and Data Science, but not interested in spending money on books, you are definitely in good space. There are a number of fantastic books and resources available online for free from top most creators and scientists. Here are such 13 best free (so far) online books and resources for learning R and Data Science from people like Hadley Wickham, Winston Chang, Garrett Grolemund and JHU Professor Roger Peng. R for Data Science, by Hadley Wickham and Garrett Grolemund, is a great book that introduces R programming, RStudio- the free and open-source integrated development environment for R, and the tidyverse, a suite of R packages designed by Wickham "to work together to make data science fast, fluent, and fun". Hadley Wickham wrote the book online and is available for free online at http://r4ds.had.co.nz/.