I have keenly been following such discussion for a while and this post is an attempt to put together the articles, books, book reviews, videos, interviews, twitter threads and so on., that I've come across in one place so it can be used as a resource. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, specifically women of color. Here is a long interview with Brian Christian and Tom Griffiths and a TED Talk with Tom Griffiths on The Computer Science of Human Decision Making. How I'm fighting bias in algorthims TED Talk -- MIT Researcher Joy Buolamwini, November 2016 More recently, April, 2017 The era of blind faith in big data must end TED Talk -- Cathy O'Neil Data is the new gold, who are the new thieves?
One thing I want you to understand is that right now, R is one of the most highly regarded, highly ranked, and fastest growing languages in existence. This IEEE ranking system uses a set of 12 metrics, including things like Google search volume, Google trends, Twitter hits, Github repositories, Hacker News posts, and more. Finally, O'Reilly media has conducted a data science survey for the last several years, and they use the survey data to analyze data science trends. To truly master data science, you'll need to learn several sub-areas like probability, statistics, data visualization, data manipulation, and machine learning.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data.
This book demonstrates statistical natural language processing methods on a range of modern applications. Do you know of other great practical books on natural language processing? This book provides an introduction to statistical methods for natural language processing covering both the required linguistics and the newer (at the time, circa 1999) statistical methods. In this post, you discovered the top books on natural language processing.
The book comprises a complete documentation of the scikit-learn library, and provides a comprehensive overview of the machine learning models and the fundamental theory needed to get started in applying ML tools in practice. Each chapter contains Python source code that cover a wide range of interesting and practical data science problems. The concepts are clearly described and their implementation is presented through useful and exciting data science problems, giving the reader a clear understanding of how to apply the ML tools on real problems. The flow of the book is constructed such that it can serve two purposes: it can be read to familiarize one with the machine learning techniques and how they are being applied on data without actually having to get into coding with Python, or it can be read as a ML course for those who want to learn ML with scikit-learn by studying the theory and applying it on real data problems throughout the reading process.
The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud computing technologies. In The Fourth Paradigm: Data-Intensive Scientific Discovery, the collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized. "The impact of Jim Gray's thinking is continuing to get people to think in a new way about how data and software are redefining what it means to do science." "I often tell people working in eScience that they aren't in this field because they are visionaries or super-intelligent--it's because they care about science and they are alive now.
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow.
Specifically these are interpersonal, intrapersonal, verbal, logical, spatial, rhythmic, naturalistic and kinaesthetic intelligence. We can expect to see many new applications that combine conventional computer science algorithms with Deep Learning to achieve sophisticated narrow intelligence applications. There is a common sentiment among Artificial General Intelligence (AGI) researchers that the research themes of Deep Learning seem to have completely missed big picture. These adaptable systems don't require the kind of high dimensional or complex inference required by that in the first theme of development.
Since this version of the book is for the Indian market, I was a bit worried about the potential differences from the U.S. 3rd edition. The U.S. edition includes: Chapter 26, "Philosophical Foundations", which covers arguments over consciousness in machines and the possibility of robot uprisings; and Chapter 27, "AI: The Present and Future", which *briefly* describes some things AI researchers need to work on before we can build a "general-purpose intelligent agent" (a.k.a. The U.S. version will say something like, "When we discussed whatzits in Chapter 4, we mentioned that they come in two flavors, X and Y", while the same line in this book will say, "Whatzits come in two flavors, X and Y". In short, if you can do without those last two chapters, buy this version and save your money.
The Hugo Awards, widely considered the most prestigious science fiction and fantasy prizes, were announced Friday, with female authors dominating and N.K. Women won both editing awards, with Ellen Datlow taking home the prize in the short form category and Liz Gorinsky winning the long form category. The Hugo Awards also honor television and movies, and this year, the film "Arrival" won for dramatic presentation, long form, beating "Ghostbusters," "Deadpool" and the first season of the television show "Stranger Things." The dramatic presentation, short form, award went to "Leviathan Wakes," an episode of the television series "The Expanse."