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[margridarnauds]

@inkandglitter21​

This is a VERY good question! And one that I think keeps quite a few people in the field up at night, to be honest, but I’m going to give the best answer I possibly can, hence why I’m giving it its own post. My apologies in advance if this gets slightly technical - Some of this is kind of inherently technical and complicated. I also am going to HAVE to mention that I’m doing my best to represent the closest thing we can get to a consensus of the field, but that doesn’t mean that someone, in a week or so, can’t publish an article that blows this out of the water. It happens. 

As a further warning, which I give every single time I discuss the issues inherent to the study of this material: I am not a religious authority. I’m a Celticist. I love the Tuatha Dé, but I can’t claim any form of spiritual connection with them. (As LGE would say, “Though the author enumerates them, she does not worship them.”) So, to anyone who reads this who might have a connection to the figures described....I can’t say anything about the relationship that you, personally, have with them. I can only say what we know, what we don’t know, and what we’re still kind of scratching our heads at with regards to the medieval material. Part of why I’ve, traditionally, sworn off talking about Bríg, Danu, and Morrigan is specifically because they tend to arouse some very strong feelings, and I never really wanted to get caught in something I couldn’t handle. 

But, also. What use is a geas if you don’t break it, likely leading you to your tragic-yet-inevitable doom? 

So, first off, let’s talk Lebor Gabála Érenn. MAGNIFICENT text, and a personal baby of mine. Chock full of information about the Tuatha Dé, the Fir Bolg, the Milesians, the High Kings of Ireland....basically everything a person could ever want to know. A mythographer’s dream and nightmare all in one. But, there’s a problem with it, and it’s one that I feel like Celticists have never stressed enough to the public, not the least because Celticists, as a group, tend to be a little....terrified of LGE. There are relatively few scholars who want to work with it after Macalister’s edition (to understand the reception to Macalister: A personal hobby of mine is collecting remarks other scholars have made about his edition, because they can be BRUTAL.) It has been described as “almost unreadable.” Which is kind of forgivable given the man was DYING when he made it, but still. 

Why are so many scholars scared of LGE? Well, primarily, because it’s hard to say that there was one singular LGE. LGE, as we know it, was compiled in about the 11th century. Or, it began to be compiled in the 11th century. It’s a Middle Irish text (so, it’s coming significantly later than, say, Tochmarc Étaíne or Cath Maige Tuired, which are both ~9th century texts, though CMT was given revisions in the 11th century to bring it in line with LGE). And it is based off of a MUCH bigger genre of pseudohistorical texts, with many of the older texts being missing or destroyed. (The one generally most mourned by us is the one in Cín Dromma Snechta, which could have dated to as early as the 8th century and definitely contained a sort of proto-LGE. We know this because LGE cites it on occasion, so the tradition didn’t fully die out, we just don’t have the full thing.) So, to begin with, LGE is a mixed bag, based off of essentially all the work that came before it, with the scribes involved basically playing a juggling game with what prior scribes jotted down. (You can see it every once in a while, where a redactor will say something like “Certain ignorant people believe ____, but it is clearly not the case, for _________.”) 

It’s almost better to view it as a scrapbook than a single text - You have about 3-4 recensions (different scholars identified different recensions) spread out over around 20 surviving manuscripts, each recension containing significant amounts of detail that vary from their counterparts. Also, studded across LGE, you have a variety of poems that are believed to date either before or at around the same time that LGE was being compiled. (Part of what drives scholars up a WALL with Macalister’s translation is that, besides not identifying the original poets for the poems featured in LGE, he also separated the poems from the text around them. And, as someone who did have to work with that translation....yeah, it is a hot mess. Sorry and RIP, Macalister, but it’s a mess.) 

Now, you might wonder: Why am I telling you this? You came at me with a mythography question and I’m hitting you with manuscript studies. But THIS is the context that it’s existing in - I know it’s fairly popular to kind of talk shit about the scribes writing this stuff down, but it’s very important to understand that they were really trying their best to understand this stuff, just like we were. And, between the various recensions of LGE, we can actually SEE the tradition evolve. One of the key ways to know that Something Pre-Christian is going on is if NONE of the redactors could agree on someone. If you see someone’s depiction REALLY shifting around, you know that the redactors were having an issue with them, possibly dealing with multiple contradictory traditions. 

Enter the Bríg/Dana/Anu/Morrigan problem. AKA “Things that will cause me to have nightmares.” So, let’s try to take this piece by piece. 

The term “Tuatha Dé Danann” is generally accepted to be a later addition. There was not, before a certain time in the Irish mythological tradition, any notion of a goddess named “Danu”. (Established by John Carey in the article, “The name Tuatha Dé Danann”-- Essentially, the term “Tuatha Dé” was the original, but then, with the influence of the term Tuatha Dé, or “Tribe of God” to refer to the Israelites, they felt they had to disambiguate it to “Tuatha Dé Danann”, or “People of Skill”, and then people mistook “Danann” as being the name of a goddess...if I remember correctly, since I don’t have it to hand at the moment.) It is very important to establish this off the bat. Now, how did this get started? And where does this web begun to be woven? Well, I feel like someone could probably write at LEAST a MA dissertation on the topic, possibly even a PhD, and it definitely isn’t going to be me, but I can try my best. 

So. The Trí Dé Dána (Three Gods of Skill). 

Originally, it seems very likely that the genitive component Dána in their name was not meant to be a proper name. They were not MEANT to be perceived as “The Three Gods of Dana”, but “the three gods of skill”. As noted by O’Rahilly (and GOD, it hurts me when he’s right), the first time we really have the phrase referenced is in Cath Maige Tuired, where, he argues, and I have to agree with him, that it refers to Goibhniu, Luchta, and Credne, who Lugh goes to for weapons to fight against the Fomoire. Additionally, you have a gloss on the 9th century text “Immacallam in Dá Thuarad: Ecna mac na tri nDea nDána” that says that their mother was Bríg, though also seems to indicate, specifically, a connection with the filid, which keeps neatly with the LGE reference (and to the image of Bríg as a poetess. I don’t have enough time to talk Bríg here, but if you want to see what I had to say a while back, I made a post here) After the 12th century, though, when the name “Danu” became associated with the Tuatha Dé, a bunch of medieval scribes looked at “Trí Dé Dána” and thought, not UNREASONABLY, “Oh? This is a reference to Danu? Let’s fix that grammar!” So you have, in some later recensions of LGE, the name “Trí Dé Dána” replaced by “Tré dée Danann/Donand/Danand.” It is vital to mention, as Williams does in Ireland’s Immortals (189), that “Danu/Donu” is never attested, it’s always Donand/Danand. So, from the get-go, trying to identify “Danand” with “Anu” was going to be problematic at best. The general consensus seems to be that Bríg and Bres were the original parents of the Trí Dé, and that it’s very possible that they were, originally, specifically associated with the filid, or poets, with this fitting very neatly into both Bres and Bríg’s associations with the Dagda, Ogma, and, of course, Elatha, but that, with Cath Maige Tuired in the 9th century and the new tradition of Bres as a tyrant, it all got muddled, with traces of it lingering into LGE. (Myth and Mythography)

But, what about “Anu?” Who is this figure? And THIS, my friends, is where things REALLY begin to get fucky. She is identified in Cormac’s Glossary as mater deorum hibernensium, “Mother of the gods of Ireland” - That is beyond doubt. This ties in very naturally with the conflation of Danand/Danu as the mother of the Trí Dé Dána that we discussed earlier. It was, to a certain extent, natural that the two of them would become intertwined.

So, this means that Anu is a genuine pre-Christian figure who became entangled up with the whole Danu business? 

Well....

Michael Clarke, in his exploration of the intellectual environment of medieval Ireland, points out that the reference to “Anu” is, in fact, VERY similar to both Isidore of Sevile and in Carolingian mythographical compilations relating to the Greek goddess Cybele, indicating that the scribe, when he was jotting that down, might have very well had that in mind (52-53). Does this mean that they invented ANOTHER goddess and then conflated that goddess with another invented goddess? 

...not quite. 

Because we still have to account for things like, for example, a mountain known as “The Paps of Anand”, which isn’t easily ascribed to a classical influence. (As noted by Mark Williams, with the typical mixture of good humor and good sense that characterizes his writing,“It beggars belief to think that the Pre-Christian Irish would not have associated so impressively breasted a landscape with a female deity.”) (189). Also, as noted by Williams, even the most skeptical argument cannot explain where Anu comes from. It seems unlikely that they would simply create a goddess out of thin air. Even Danu, as sketchy as her existence is, came from SOMEWHERE, even if it was a linguistic, instead of spiritual, basis. But THEN we have to deal with another question: If this figure is so important, why doesn’t she show up in any of the myths? Why let the Dagda, Lugh, the Morrigan, Midir, Óengus, Ogma, and Nuada have all the fun? The Dagda in particular is as close to a BLATANTLY pre-Christian deity as you can get on-page, so it can’t be chalked up to a simple “They didn’t want to depict the mother of the gods on page.” Mark Williams suggests, tentatively, that Anu might have been a minor Munster figure who swelled in popularity, possibly dropped in by some Munster-based scribes who wanted to bolster their own province’s reputation and, equally tentatively, without further evidence to go on, I have to agree with him. I believe there’s too much evidence to suggest that there was SOMETHING, but that there’s also too little to say that she had the range or influence described, and that it’s very likely that, at the very least, the scribe writing that entry had Cybele on his mind. It’s really, really a mystery, though. 

Furthermore, as John Carey notes in “Notes on the Irish War Goddess”....why conflate Anu with the Morrigan? “While it may be plausible....to explain a war-goddess’s possession of sexual characteristics...it is considerably more difficult to follow that chain of thought in reverse in order to account for a land goddess with martial traits. Not is there any evident reason for a conflation of Anu/Anann and the Morrígan unless the former were to some extent linked with war already” pointing out that, relevant to the first paragraph of this, it SEEMS like her inclusion among the daughters of Ernmas was forced on the redactor by a prior tradition (271). Sometimes, she’s a fourth daughter of Ernmas, sometimes she’s a replacement for the Morrigan, sometimes, in the later texts, she’s associated with Danu. It’s like the various authors KNEW they had to include her in there somehow, but they didn’t know how, and she didn’t fit in smoothly once they did. Are we looking at a war/land goddess , obscure enough that the redactor didn’t know where to put her, deciding that she HAD to be the Morrigan/one of the Morrigan’s sisters but not knowing exactly how to fit her in? It wouldn’t be the first time multiple traditions clashed like this. Also, as noted by Sharon Paice Macleod, who gave a very thorough (if not always, in my opinion, sufficiently contextual) account of the tradition, there is a location called the “Paps of the Morrigan”, further suggesting a fertility aspect to the Morrigan that also features into Carey’s earlier argument of dual aspects to the Irish war goddess, along with Bhreatnach’s suggestion of the sovereignty goddess, who represents the land in the medieval Irish literary tradition (and into the present) also functioning as a goddess of death. (Indeed, as noted by Bhreatnach, the hag Cailb from Togail Bruidne Dá Derga, who functions as a sort of anti-sovereignty goddess, identifies herself with Nemain and Badb, at 255. Sovereignty giveth, sovereignty taketh away when you don’t fulfill your place as king.)

Basically, as with almost everything relating to pre-Christian religion in Ireland, we’ve really, really got to shrug our shoulders and go “Fuck if I know, mate.” 

My best attempt at a tl;dr for...this: 

LGE - WEIRD 

Danu - Help us. 

Trí Dé - Who’s your daddy? (Most likely? Bres originally, though it got out of hand after, like, the 12th century.) 

Anu - Who are you? (Who, Who?) 

Sources: 

Scowcroft, “Leabhar Gabhála Part I: The growth of the text" (For the discussion on the different recensions of LGE.)

John Carey, “The Irish National Origin-Legend: Synthetic Pseudohistory”

T.F O’Rahilly, Early Irish History and Mythology

Máire Bhreathnach, “The Sovereignty Goddess as Goddess of Death”

John Carey, “The name Tuatha Dé Danann”

Mark Williams, Ireland’s Immortals (Who, really, puts this all together in a so much more cohesive way in his book, I highly recommend it to anyone who wants to get an idea of how these things develop.)

John Carey, “Myth and Mythography in Cath Maige Tuired.” 

Michael Clarke, “Linguistic Education and Literary Creativity in Medieval Ireland”. 

John Carey, “Notes on the Irish War Goddess”

Sharon Paice Macleod, “Mater Deorum Hibernensium: Identity and Cross-Correlation in Early Irish Mythology.” 

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[derrickcodes]

Hi, can you help me, if you have some time? I’m in college and I’m supposed to choose my specialty in like a month, but I still don’t know what do I want to do. I feel like there’s so much to learn and I don’t want to miss out on anything. Can you tell me what should I expect from working with different languages? (I’ve tried only like two so far) or do you have any tips which would help me figure it out? Please, you’d literally help save my future (dramatic, I know, sorry xD).

Sure! I understand your sentiment completely. Computer Science is such a vast field, it can feel overwhelming with how much there is to learn. I was in that same boat for the first three years of my comp sci degree and I still don’t fully know what I want to do.

The great thing about computer science is that while it is a relatively new field, it has spread its wings and has branched out in so many ways and has even affected other areas of study. Here are 10 common specializations, what they do, and what some code might look like (when possible):

Software development is what people tend to think of when studying computer science. This typically involves wanting to work in the industry as someone who develops code based on what a client or company wants. You will take courses about the software development process, such as software testing and agile development. There aren’t really any languages I would recommend, since this is such a broad field, but good places to start are C++, Java, C#, and Python. If anything, I would suggest reading further, since software development can be broken down into the other categories. An example of Java code can be seen below (and C++ and C# basically look like this as well).

Game development is another topic people think of with computer science. A lot of our generation grew up playing video games and somewhere along the line thought that they would want to develop games as well. Game developers need to have a good understanding of computer graphics (such as using OpenGL), physics, and computer programming in C++ and C#. A great place to start is looking into Unity. It’s free, it’s easy to use, and it’s what a lot of industry people use today.

Web development has been, currently is, and will always be in high demand. Most interactions people have with computers are through websites, so of course there’s a lot of companies whose development revolves around websites. The standard languages to learn are HTML, CSS, and JavaScript, although if you want an edge up, look into JavaScript libraries and frameworks, like Angular and Node.js. Also, W3Schools will be your best friend. It’s hard to show examples of this that aren’t hundreds of lines long, so here’s a little example showing HTML, CSS, and JavaScript similar to a W3Schools example.

Data science is exploding right now. The world has so much data and we’re just now beginning to analyze all of it. Say you have the history of every user that has ever been shown your ad and who clicked on it and when. Could you use that to determine anything about the effectiveness of the ad, time of day, where it’s displayed, and if they’ll click again? That’s data science. Typical courses include Statistical Computing, Data Mining, and Machine Learning. Typical languages for data science include R and Python. One subtopic that’s really big is machine learning. Can you take the data that you have and have a program “learn” off that data and make predictions about the future? Take a look at this Python code that analyzes a data set and is able to predict whether or not breast cancer is present based on a few attributes:

Information systems is the foundation of both web development and data science, as it involves how and where we store our information and data. You’ll study database management and possibly some cloud storage, since this is usually where we store things. You will want a strong understanding of data structures if you really want to learn the best ways to store things (I’ll give you a hint, databases usually use a variation of Binary Search Trees). You’ll also learn how to retrieve and manipulate the data that is stored. The languages you’ll want is SQL (rather MySQL or NoSQL) and PHP. Some MySQL code for creating a schema with tables will look like this.

Computer engineering is a close friend of computer science, but is mostly focused on the hardware side of things. Computer engineering is all about how you build the computer system. You will spend a lot of time learning the physics that goes into computer design, namely electricity and magnetism. Some classes would include Circuit Analysis, Signals, and Digital Systems, but a lot of it is up to you.

Systems & Architecture is similar to computer engineering, as you’re still focused on being close to the hardware, but you’re more interested in the software side. This was my favorite section of computer science, because you get to learn about computers from a brand new perspective and see how they work down to the electricity flowing through it. Typical courses include Computer Architecture, Operating Systems, Parallel Systems, and the like. You will learn languages like C and Assembly so you can get a good grasp of how fast and powerful a computer can be since you’re almost talking directly to it. For example, this C code is typical practice for interacting with dynamic libraries.

Theoretical computer science is a very intriguing study. Instead of learning about how all these different languages can be applied, you look into what computers are actually capable of. The main courses in any theoretical computer science section are Programming Language Theory, looking into how can you design and classify a programming language, Algorithm Analysis and Design, the different paradigms used to solve different problems, and Theory of Computation, studying what problems can be solved by computers and how quickly can they be solved. Studying this is a good way to get a job in the government, as organizations like the NSA are always looking for people to work on cryptography, which has a strong background in theory.

Scientific computing is the mix of computer science and applied mathematics. You take your understanding of programming and mathematical theory to create computer algorithms to solve problems as fast as they can (and maybe faster than ever before)! You’ll want to have a very strong understanding of linear algebra (the study of matrices), since a lot of computational tasks can be done effectively and efficiently using matrices. Typical courses include Numerical Linear Algebra, Numerical Analysis, and Partial Differential Equations. For this, languages like MATLAB (or its free counterparts Octave or Scilab), Mathematica, and even Fortran are your best bets. A typical career can involve becoming a researcher, or working for a company that relies on the most optimized mathematical code, such as a government agency or somewhere in the finance world. Here’s an example of some code written in Octave to analyze a waveform and reproduce it as a series of numbers (hey, I did a post about this earlier!)

Bioinformatics is the love child of computer science and biology. In this study, you will use what you know about computer science and programming to better understand biological data. You can use this to study the human body, such as the human genome, so we as humans can have a better understanding of what makes us human, or you can apply it and develop medical software. One of my friends got a PhD in bioinformatics and she now writes the software for heart monitors. Since this is somewhat similar to data science, you’ll want to learn Python and R.

There are more specializations, like computer security or networking, but these are the 10 I’m most familiar with. I hope this helped and feel free to reach out to me if you have any more questions!

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Master of Science or MSc in information technology or IT is a 2 years long postgraduate level master's degree program. MSc IT aspires to provide academic as well as practical knowledge on topics like software development, data mining, computer systems, analytics etc.

The prospects willing to join the MSc IT program should have a Bachelor’s degree in applicable fields like BSc in IT/ CS, BCA, BE/ BTech in IT or CS from a recognized university. They must also score a minimum of 50 per cent marks in graduation to be qualified for the course. 

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The MSc IT Syllabus contains important topics like web development, OS, database management, project management, cybersecurity, object-oriented programming languages and other related concepts.

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For both BSc IT courses and MSc, It courses Auro University is one of the best choices. From quality education to placements and infrastructure, you will be provided with every necessary thing for a student.

If the student wants to opt for higher studies preferably than doing the prevalent MSc IT jobs, there are many choices available. MSc IT degree allows students to pursue an MPhil or a PhD in IT and corresponding fields. If the student is wanting to elevate to administrative positions, they can study MBA in Information Technology.

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Definition: Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web Server is designed to serve HTTP Content. A web server is a specialized type of…

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Top 20 Research Paper Writing Services Of 2020 It is feasible to see professor’s happiness and shock using this service. Additionally, college students’ save their nervous system. The firm claims that their service is cheap and pleasant. I may have written it myself, however I didnt assume my writing expertise would have helped me in securing a spot in my dream university. But with their assist, I not only received admission within the university but also made my approach to the principals distinctive students listing. My parents are tremendous happy with me, and its all because of WritersThrone. PapersOwl is a accountable service provider, which focuses on college students’ wants. The service invitations college students to barter their particular person instances. Many writers get carried off by making an attempt to put in writing too much in too little time. Students can afford using this service each time they lack time or power for doing homework. The web site has the so-known as Customer Bill of Rights, which discusses the main benefits of cooperating with this writing service. The service has its personal plagiarism analyzer, which ensures originality of content material you obtain. Be it to construction a thesis paper or researching about tips on how to write a case-study; they always end up with way-lot-of questions and lack of ‘know-how’ on making a sensible method. I submitted it to their writers they usually did a superb job. They have successfully earned a loyal customer with their unwavering great providers. No matter what time of the day it's, they are all the time keen to provide me with amazing providers. Firstly, we've a whole group of writers and editors who work only on their specific assignments. The folks employed for this specific task are PhD / MSc / MBA / B.tech Data Analyst of their private areas. Moreover, we have on board a large number of freelancers thereby making the task handy on our part. What do I even have to do to be able to get the most effective grades for my University Assignments? Its very simple, all you must do is, place your subsequent task through Assignment Studio, and certainly one of our expert task writers will work on it. Several college students needprogramming project helpin these areas, and they seek assignment helper. We create the code, with detailed feedback in order that students understand the mechanism of the code, and also connect screenshots of the output generated. So, if you wish to buyassignments onlinein programming topic, then contact the shopper care executives of assignments4u as soon as potential to get your job done. However, most of the instances, scholar discover themselves in ‘pools’ of confusion while undertaking such course works. There is no doubt about the authenticity of their companies because they have all the time provided me with impressive paperwork. When it comes to dissertation writing, I solely discovered them an excellent agency. They are offering glorious providers, and the quality of the dissertation is always excellent. I even have recommended this website to many pals of mine. I needed them to write down an admission essay for me.

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19 Academic Search Engines

I had the chance to stumble upon a Facebook post by UNAM con Conciencia called 19 Buscadores Académicos que todo Tesista debe Conocer. Here’s the list with a brief description. Note: All credit goes to UNAM con Conciencia. The translation of the description is mine.

Dialnet focused on magazines, theses, scientific conferences, and such. Has links to different authors, gathers their work even some quotes. Very helpful in journalistic research, as a documentary source.

Scielo created with the purpose of giving visibility to scientific literature, mostly from the Caribbean and Latin America. Is currently supported by several foundations and associations from all over the world.

ISeek centers on University resources, ONGs, non-profit organizations, and official websites.

Eric virtual library specialized on academic issues. Is a database the American government created on 1964 which contains a variery of references: papers, magazines, and published work. Has an advanced search engine.

Academia.edu users and researchers have the chance to publish their research and essays, and to follow other members with common interests. Is a search engine similar to a social network since it allows interaction between users, such as profile activity, number of visits, followers, comments, etc.

Biology Browser focused on researchers on the field of biology and its current branches. Is made by Reuters and has a news section.

RefSeek its results contain verified web pages resources, books, encyclopedias, newspaper, magazines, research, and published work.

Science Research public and free. Uses different specialized search engines and is capable of avoiding duplicity, selecting useful information, verify it, and more. Has an auto-tour to learn how to use it.

Jurn has over 3000 specialized on arts an humanities magazines. Is a search engine which indexs academic titles and articles, and doctoral theses on several fields as art, ecology, economy, biomedic science, linguistics, and general humanities.

Teseo perfect for students who are on PhD classes and have to write their thesis, as Teseo tells which are the over investigated topics. Is a PhD theses search engine developed by Ministerio de Educación, Cultura y Deportes of Spain.

Redalyc is a scientific hemerotec everyone can acces to. Recetly added a section where you can create a profile and identify certain work.

Chemedia the best about Chemedia is that its resources (documents, articles, papers, and others) can be downloaded on PDF format. Now is ADreamUp, and it seems to be a consulting agency on technology, so cross this one out of the list, unless, of course, you’re curious and want to know about ADreamUp.

PDF SB website where you can read and download e-books for free on PDF. Has specific content on several field investigations and in different languages. Its database has over 71.600 books.

CERN Document Server digital file which features articles, reports and other multimedia content.

World Wide Science holds content from the world and shows the results in a selective way, this is, importance order.

Highbeam Research has an specialize database for professionals and students of different fields. Is a system that includes articles, book quotes, published researchs, and specialized and academic magazines.

Science search engine which has 60 databases from 200 millions of specialized websites on scientific information.

Microsoft Academic Search has over a thousand indexed publictions but is also capable of showing how certain elements are related, a useful trait when it comes to find similar material from different authors who follow similar theories, or studies about an specific topic delimited to a year an field of study. Note from the official website: The original Microsoft Academic Search has been completely decommissioned. Cross this one out too.

Google Scholar contains theses, summaries and books. At the same time allows you to find related referentes.

That’d be the list, 17 useful search engines in total. Use them wisely.

Note 2: Here’s a page where you can find the same list with more description in Spanish. An honorable mention to a comment that says Yahoo Answers should be on the list.

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Illustration Photo: Autonomous transport robot Omron (credits: PressReleaseFinder / Flickr Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Generic (CC BY-NC-ND 2.0))

Call for Proposals: Amazon Research Awards Program for Colleges or Universities that grant PhD degrees in fields related to Machine Learning

Amazon Research Awards (“ARAs”) are structured as a one-year unrestricted gifts to academic institutions. ARA funding amounts will be determined by Amazon in its sole discretion. ARA funding is not extendable or transferable, but you may submit new proposals for subsequent ARA Program calls.

Focus Areas

Computer vision Recognition: categorization, detection, segmentation Visual search Deep neural network compression and optimization Video understanding: actions, events Large-scale data annotation Computer vision for apparel Human body: detection, tracking, pose Motion: segmentation, tracking 3D modeling: structure-from-motion, slam, stereo and reconstruction Computational photography Computer vision for robotics Faces and gestures Image and video captioning

Fairness in artificial intelligence Transparency, explainability, and accountability in AI systems Theories of computational/algorithm fairness and factors that affect algorithmic trustworthiness Ethical decision-support and decision-making systems Detecting and ameliorating adverse biases in data and algorithms, and fairness-aware design of algorithms Metrics and methods for designing, piloting, and evaluating systems that mitigate against adverse biases and ensure fairness, including the use of human-machine collaboration and decision support Statistical methods for detecting bias in systems as they are operating

Knowledge management and data quality Data cleaning for machine learning Graph mining from knowledge graphs and user behaviors Knowledge embedding Knowledge extraction from unstructured and semi-structured data Knowledge verification Knowledge-based search Large-scale data alignment and integration Leveraging structured knowledge in deep learning and recommendation Quantitative and logical error detection

Machine learning algorithms and theory Active learning and data cleaning Data and resource efficient learning Deep learning and representation learning Fair, explicable and interpretable learning Transfer and meta-learning Online and continual learning Parallel and distributed Learning Robust and privacy preserving learning Reinforcement learning

Natural language processing Advances in neural MT for noisy and user-generated content Chatbots and dialogue systems Detection of inappropriate content Efficient training and fast inference for neural MT Context-aware MT Explainability in neural NLP methods Fact extraction, verification and trustworthiness in unstructured data Multitask and reinforcement learning for MT Named entity translation and transliteration NLP applications in search Question answering Text summarization Narrative understanding Common sense inference

Online advertising AI methods for online advertising Algorithmic marketing Large scale experimentation and testing Learning mechanisms Measurement of brand advertising Online algorithms for targeting, bidding and pricing Optimizing for long term objectives Prediction, forecasting and automated decision making in ad systems Structure of advertising marketplaces

Operations research and optimization Assortment management Management of warehouse operations Marketplace design: incentives/policies for increasing efficiency and growth in a multi-agent marketplace Strategic supply chain management: network design/topology Tactical supply chain management: vendor management (including supplier contract negotiation and procurement), inventory buying, inventory deployment, demand fulfillment Transportation: long-haul operations (including airline operations), last-mile operations Other supply chain optimization topic

Personalization Approaches to estimate quality of recommenders using abundant implicit and sparse explicit feedback Detecting and responding to spam in behavioral data to protect customers in recommendation contexts Scalable NLP approaches for search query understanding for non-English Scalable approaches to detect incorrect catalog information Approaches to identity synonyms in noisy product catalog Item-to-item collaborative filtering using deep learning

Robotics Affective and social interactions Autonomous navigation and mobility Dexterous and reactive manipulation Human machine interaction and collaboration Machine learning and learning from human preferences Motion planning Multi-robot systems and multi-agent pathfinding Object detection and pose estimation Sample-efficient reinforcement learning Semantic scene understanding for robotics Simulation and sim to real transfer SLAM and long-term autonomy Theoretical advances as well as practical applications

Search and information retrieval Multilingual language understanding Conversational search

Security, privacy and abuse prevention ML for malware analysis and detection Browser/device fingerprint and digital forensics Early detection of emerging patterns with limited labeled data (one-shot-learning) Fraud detection and prevention Graph modeling (latent representations from a graph and anomaly detection) Human-in-the-loop machine learning Online and adaptive machine learning Web behavioral modeling, online identity and password-less authentication Threat and intrusion detection for cloud security ML for obfuscation detection from text, image and online behaviors Detection and tracking of online adversarial attempts

Dateline for submission: October 4, 2019, 11:59 PT.

Check more https://adalidda.com/posts/hSXtrWahpN8qHRjQP/call-for-proposals-amazon-research-awards-program-for

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Text Mining and Natural Language Processing in R ##UdemyFrancais ##UdemyOnlineCourse #Language #Mining #Natural #Processing #Text Text Mining and Natural Language Processing in R Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media? Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends? Over the past decade there has been an explosion in social media sites and now sites like Facebook and Twitter are used for everything from sharing information to distributing news. Social media both captures and sets trends. Mining unstructured text data and social media is the latest frontier of machine learning and data science.  LEARN FROM AN EXPERT DATA SCIENTIST  WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Unlike other courses out there, which focus on theory and outdated methods, this course will teach you practical techniques to harness the power of both text data and social media to build powerful predictive models. We will cover web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. Additionally you will learn to apply both exploratory data analysis and machine learning techniques to gain actionable insights from text and social media data . TAKE YOUR DATA SCIENCE CAREER TO THE NEXT LEVEL BECOME AN EXPERT IN TEXT  MINING & NATURAL LANGUAGE PROCESSING : My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common social media mining and natural language processing packages to extract insights from text data.   I will even introduce you to some very important practical case studies - such as identifying important words in a text and predicting movie sentiments based on textual  reviews. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful  course, you’ll know it all:  extracting text data from websites, extracting data from social media sites and carrying out analysis of these using visualization, stats, machine learning, and deep learning!   Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.  HERE IS WHAT YOU WILL GET: Data Structures and Reading in R, including CSV, Excel, JSON, HTML data. Web-Scraping using R Extracting text data from Twitter and Facebook using APIs Extract and clean data from the FourSquare app Exploratory data analysis of textual data Common Natural Language Processing techniques such as sentiment analysis and topic modelling Implement machine learning techniques such as clustering, regression and classification on textual data Network analysis Plus you will apply your newly gained skills and complete a practical text analysis assignment We will spend some time dealing with some of the theoretical concepts. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW! Who this course is for: People who wish to learn practical text mining and natural language processing People with prior experience of using RStudio People with some prior experience of implementing machine learning techniques in R People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course People who wish to derive insights from textual and social media data 👉 Activate Udemy Coupon 👈 Free Tutorials Udemy Review Real Discount Udemy Free Courses Udemy Coupon Udemy Francais Coupon Udemy gratuit Coursera and Edx ELearningFree Course Free Online Training Udemy Udemy Free Coupons Udemy Free Discount Coupons Udemy Online Course Udemy Online Training 100% FREE Udemy Discount Coupons https://www.couponudemy.com/blog/text-mining-and-natural-language-processing-in-r/

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Text Mining and Natural Language Processing in R

Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media?

Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends?

Over the past decade there has been an explosion in social media sites and now sites like Facebook and Twitter are used for everything from sharing information to distributing news. Social media both captures and sets trends. Mining unstructured text data and social media is the latest frontier of machine learning and data science.

LEARN FROM AN EXPERT DATA SCIENTIST  WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Unlike other courses out there, which focus on theory and outdated methods, this course will teach you practical techniques to harness the power of both text data and social media to build powerful predictive models. We will cover web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. Additionally you will learn to apply both exploratory data analysis and machine learning techniques to gain actionable insights from text and social media data .

TAKE YOUR DATA SCIENCE CAREER TO THE NEXT LEVEL

BECOME AN EXPERT IN TEXT  MINING & NATURAL LANGUAGE PROCESSING :

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common social media mining and natural language processing packages to extract insights from text data.   I will even introduce you to some very important practical case studies - such as identifying important words in a text and predicting movie sentiments based on textual  reviews. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful  course, you’ll know it all:  extracting text data from websites, extracting data from social media sites and carrying out analysis of these using visualization, stats, machine learning, and deep learning!  

Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.

HERE IS WHAT YOU WILL GET:

Web-Scraping using R

Extracting text data from Twitter and Facebook using APIs

Extract and clean data from the FourSquare app

Exploratory data analysis of textual data

Common Natural Language Processing techniques such as sentiment analysis and topic modelling

Implement machine learning techniques such as clustering, regression and classification on textual data

Network analysis

Data Structures and Reading in R, including CSV, Excel, JSON, HTML data.

Plus you will apply your newly gained skills and complete a practical text analysis assignment

We will spend some time dealing with some of the theoretical concepts. However, majority of the course will focus on implementing different techniques on real data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!

Who this course is for:

People who wish to learn practical text mining and natural language processing

People with prior experience of using RStudio

People with some prior experience of implementing machine learning techniques in R

People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course

People who wish to derive insights from textual and social media data.

94% off !!! #udemy #course Text #Mining and Natural #Language Processing in R Hands-On Text Mining and Natural Language Processing (NLP) Training for Data Science Applications in R #couponcode https://www.udemy.com/text-mining-and-natural-language-processing-in-r/?couponCode=TEXTMINING_DISC1

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Data Science:Data Mining & Natural Language Processing in R

MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R:

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge.    

                              LEARN FROM AN EXPERT DATA SCIENTIST  WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning. This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data.

                                 NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data.   I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks!  

The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.

HERE IS WHAT YOU WILL GET:

(a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools.  

(b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling.  

(c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation.   (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques.  

(e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results.

More Specifically, here's what's covered in the course:

Getting started with R, R Studio and Rattle for implementing different data science techniques

Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data.

How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc

Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE

Statistical analysis, statistical inference, and the relationships between variables.

Machine Learning, Supervised Learning, & Unsupervised Learning in R

Neural Networks for Classification and Regression

Web-Scraping using R

Extracting text data from Twitter and Facebook using APIs

Text mining

Common Natural Language Processing techniques such as sentiment analysis and topic modelling

We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!

Who this course is for:

Students wishing to learn practical data science and machine learning in R

Students wishing to learn the underlying theory and application of data mining in R

Students interested in obtaining/mining data from sources such as Twiter

Students interested in pre-processing and visualizing real life data

Students wishing to analyze and derive insights from text data

Students interested in learning basic text mining and Natural Language Processing (NLP) in R.

94% off !!! #udemy #course for #Data #Science :Data Mining & Natural Language Processing in R Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples #coupon #deal https://www.udemy.com/data-science-datamining-natural-language-processing-in-r/?couponCode=DATAMINE1

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Definition: Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web Server is designed to serve HTTP Content. A web server is a specialized type of…

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Hands-on text mining and natural language processing (NLP) training for data science applications in R

Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media?

Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends?

Over the past decade there has been an explosion in social media sites and now sites like Facebook and Twitter are used for everything from sharing information to distributing news. Social media both captures and sets trends. Mining unstructured text data and social media is the latest frontier of machine learning and data science.

LEARN FROM AN EXPERT DATA SCIENTIST  WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Unlike other courses out there, which focus on theory and outdated methods, this course will teach you practical techniques to harness the power of both text data and social media to build powerful predictive models. We will cover web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. Additionally you will learn to apply both exploratory data analysis and machine learning techniques to gain actionable insights from text and social media data .

TAKE YOUR DATA SCIENCE CAREER TO THE NEXT LEVEL

BECOME AN EXPERT IN TEXT  MINING & NATURAL LANGUAGE PROCESSING :

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common social media mining and natural language processing packages to extract insights from text data.   I will even introduce you to some very important practical case studies - such as identifying important words in a text and predicting movie sentiments based on textual  reviews. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful  course, you’ll know it all:  extracting text data from websites, extracting data from social media sites and carrying out analysis of these using visualization, stats, machine learning, and deep learning!  

Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.

HERE IS WHAT YOU WILL GET:

Web-Scraping using R

Extracting text data from Twitter and Facebook using APIs

Extract and clean data from the FourSquare app

Exploratory data analysis of textual data

Common Natural Language Processing techniques such as sentiment analysis and topic modelling

Implement machine learning techniques such as clustering, regression and classification on textual data

Network analysis

Data Structures and Reading in R, including CSV, Excel, JSON, HTML data.

Plus you will apply your newly gained skills and complete a practical text analysis assignment

We will spend some time dealing with some of the theoretical concepts. However, majority of the course will focus on implementing different techniques on real data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!

Who this course is for:

People who wish to learn practical text mining and natural language processing

People with prior experience of using RStudio

People with some prior experience of implementing machine learning techniques in R

People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course

People who wish to derive insights from textual and social media data.

94% off !!! #udemy #course Text #Mining and Natural #Language Processing in R Hands-On Text Mining and Natural Language Processing (NLP) Training for Data Science Applications in R #couponcode https://www.udemy.com/text-mining-and-natural-language-processing-in-r/?couponCode=TEXTMINING_DISC1

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Hands-on text mining and natural language processing (NLP) training for data science applications in R

Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media?

Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends?

Over the past decade there has been an explosion in social media sites and now sites like Facebook and Twitter are used for everything from sharing information to distributing news. Social media both captures and sets trends. Mining unstructured text data and social media is the latest frontier of machine learning and data science.

LEARN FROM AN EXPERT DATA SCIENTIST  WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Unlike other courses out there, which focus on theory and outdated methods, this course will teach you practical techniques to harness the power of both text data and social media to build powerful predictive models. We will cover web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. Additionally you will learn to apply both exploratory data analysis and machine learning techniques to gain actionable insights from text and social media data .

TAKE YOUR DATA SCIENCE CAREER TO THE NEXT LEVEL

BECOME AN EXPERT IN TEXT  MINING & NATURAL LANGUAGE PROCESSING :

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common social media mining and natural language processing packages to extract insights from text data.   I will even introduce you to some very important practical case studies - such as identifying important words in a text and predicting movie sentiments based on textual  reviews. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful  course, you’ll know it all:  extracting text data from websites, extracting data from social media sites and carrying out analysis of these using visualization, stats, machine learning, and deep learning!  

Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.

HERE IS WHAT YOU WILL GET:

Web-Scraping using R

Extracting text data from Twitter and Facebook using APIs

Extract and clean data from the FourSquare app

Exploratory data analysis of textual data

Common Natural Language Processing techniques such as sentiment analysis and topic modelling

Implement machine learning techniques such as clustering, regression and classification on textual data

Network analysis

Data Structures and Reading in R, including CSV, Excel, JSON, HTML data.

Plus you will apply your newly gained skills and complete a practical text analysis assignment

We will spend some time dealing with some of the theoretical concepts. However, majority of the course will focus on implementing different techniques on real data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!

Who this course is for:

People who wish to learn practical text mining and natural language processing

People with prior experience of using RStudio

People with some prior experience of implementing machine learning techniques in R

People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course

People who wish to derive insights from textual and social media data.

94% off !!! #udemy #course for #Data #Science :Data Mining & Natural Language Processing in R Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples #coupon #deal https://www.udemy.com/data-science-datamining-natural-language-processing-in-r/?couponCode=DATAMINE1