Jurnal Singkat

http://nieluj.github.io/flareon2/

https://www.oreilly.com/learning/how-to-build-a-robot-that-sees-with-100-and-tensorflow Object recognition is one of the most exciting areas in machine learning right now. Computers have been able to recognize objects like faces or cats reliably for quite a while, but recognizing arbitrary objects within a larger image has been the Holy Grail of artificial intelligence. Maybe the real surprise is that human brains recognize objects so well. We effortlessly convert photons bouncing off objects at slightly different frequencies into a spectacularly rich set of information about the world around us. Machine learning still struggles with these simple tasks, but in the past few years, it’s gotten much better. Deep learning and a large public training data set called ImageNet has made an impressive amount of progress toward object recognition. TensorFlow is a well-known framework that makes it very easy to implement deep learning algorithms on a variety of architecture

http://blog.apnic.net/2016/09/15/quiet-ipv4-internet/

http://theiff.org/oexhibits/paper02.html Robert Lang is a pioneer in the emerging field of computational origami, a branch of mathematics that explores the formal properties and potentialities of folded paper. Like the study of knots, pioneered in the late nineteenth century, computational origami and its practical offshoot origami sekkei or “technical folding” turn out to have a surprising range of applications to real world problems; from working out how to fold up stents so they can be threaded into arteries, to designing thin-film telescopes that are packed into the hold of a space shuttle. Lang is the inventor of the TreeMaker computer program, which allows him to design and calculate crease patterns for a wide range of origami models—including intricate insects, crustaceans, and amphibians. He has been one of the very few

https://www.academia.edu/12313996/Judul_Berita_Clickbait_Akibatkan_Degradasi_Jurnalistik?auto=download https://github.com/TJkrusinski/clickbait https://github.com/peterldowns/clickbait-classifier http://www.kompasiana.com/afsee/media-mengenali-jebakan-klik_54f77e62a333112c6f8b45d8 http://www.romelteamedia.com/2014/08/jebakan-klik-click-bait-modus-media.html http://www.nkritoday.com/berita/catat-waspada-jebakan-spam-bot-jangan-pernah-klik-siapa-intip-facebook-kamu.html https://www.academia.edu/12313996/Judul_Berita_Clickbait_Akibatkan_Degradasi_Jurnalistik?auto=download

https://www.wired.com/2016/05/the-end-of-code/ Before the invention of the computer, most experimental psychologists thought the brain was an unknowable black box. You could analyze a subject’s behavior— ring bell, dog salivates —but thoughts, memories, emotions? That stuff was obscure and inscrutable, beyond the reach of science. So these behaviorists, as they called themselves, confined their work to the study of stimulus and response, feedback and reinforcement, bells and saliva. They gave up trying to understand the inner workings of the mind. They ruled their field for four decades. Then, in the mid-1950s, a group of rebellious psychologists, linguists, information theorists, and early artificial-intelligence researchers came up with a different conception of the mind. People, they argued, were not just collections of conditioned responses. They absorbed information, processed it, and then acted upon it. They had systems for writing, storing, and recalling memorie

https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c#.2tl34q1ew

https://github.com/datasciencemasters/go

https://www.quantstart.com/articles/How-to-Learn-Advanced-Mathematics-Without-Heading-to-University-Part-2 In the last article in the series we looked at the foundational courses that are often taken in a four-year undergraduate mathematics course. We saw that the major courses were Linear Algebra, Ordinary Differential Equations, Real Analysis and Probability. In the "second year" of our self-study mathematics degree we'll be digging deeper into analysis and algebra, with discussions on the Riemann integral, abstract algebra, metric spaces and vector calculus. In a formal setting the midpoint of Year 2 is where students begin to get a feel for whether they want to specialise in either pure or applied mathematics, and whether they wish to concentrate on analysis or algebra. Pure mathematics and algebra are quite synonymous, as are analysis and applied mathematics. The former, because advanced pure mathematics is often concerned with symmetry and relatio

https://www.quantstart.com/articles/How-to-Learn-Advanced-Mathematics-Without-Heading-to-University-Part-1 I am often asked in emails how to go about learning the necessary mathematics for getting a job in quantitative finance or data science if it isn't possible to head to university. This article is a response to such emails. I want to discuss how you can become a mathematical autodidact using nothing but a range of relatively reasonably priced textbooks and resources on the internet. While it is far from easy to sustain the necessary effort to achieve such a task outside of a formal setting, it is possible with the resources (both paid and free) that are now available. We'll begin by discussing the reasons for wanting to learn advanced mathematics, be it career-driven, to gain entrance into formal education or even as a hobby. We'll then outline the time commitment required for each stage of the process, from junior highschool (UK GCSE equivalent) thro