If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This repo contains an incremental sequence of notebooks designed to teach deep learning, Apache MXNet (incubating), and the gluon interface. Our goal is to leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place. If we're successful, the result will be a resource that could be simultaneously a book, course material, a prop for live tutorials, and a resource for plagiarising (with our blessing) useful code. To our knowledge there's no source out there that teaches either (1) the full breadth of concepts in modern deep learning or (2) interleaves an engaging textbook with runnable code. We'll find out by the end of this venture whether or not that void exists for a good reason.
Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people manage and analyze vast amount of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans for humans.
In this tutorial, we will demonstrate how to create scalable, end-to-end data analysis processes in R on single machines as well as in-database in SQL Server and on Hadoop clusters running Spark. We will provide hands-on exercises as well as code in a public GitHub repository for attendees to adopt in their data science practice. In particular, the attendees will see how to build, persist, and consume machine learning models using distributed machine learning functions in R.
Ease of use and interpretability have made logistic regression and decision trees analytic staples. But their accuracy and classification stability leave something to be desired. So the industry keeps searching for an algorithm that can decipher key patterns and signals in data. A long line of fad techniques has come and gone. Deep learning is the latest darling of the data science set.
Don't worry, robots and AI won't take your job: Well, at least not all of it Automation probably won't lead to massive unemployment, but governments will still need to prepare for major upheaval, according to a new study. Until now, automation has gotten a bad rap. In 2013, Oxford professors Carl Frey and Michael Osborne analyzed 702 occupations and declared 69 million US jobs, or 47 percent of the workforce, would be lost. In 2017, CNBC reported that 65 percent of Americans believed other industries would suffer because of automation, but theirs would be unaffected. Despite the doomsaying being flat-out wrong (automation will only eliminate 9 percent of US jobs in 2018 but also create 2 percent more in a new "automation economy"), the demand for automation has never been higher.
Great article but it unfortunately doesn't account for a lot of the physics that go into weather prediction:( that's what I'm hoping for soon (and trying to go to grad school / phd programs for). You can only do so much (right now!!) with ML, NNs, and regression when it comes to weather prediction as, again, you're lacking a lot of the physics in those models.