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Top 10 Reasons to Use Technology in Education: iPad, Tablet, Computer, Listening Centers Tweet this video! clicktotweet.com www.ampli.com Please like us on Facebook: www. .facebook.com For the Article in Print, visit blog.ampli.com While there are some nay-sayers who bring up some negative effects of technology on education in the classrooms, mainly budgetary reasons, using new technologies is the future of the education system. The following reasons should silence the nay-sayer and help them heed the wisdom from Sheryl Nussbaum-Beach, "Teachers will not be replaced by technology, but teachers who don't use technology will be replaced by those who do.". Teachers are finding that using different classroom technologies like tablet computers, digital video, iPods, and video games are great tools for helping students learn. Kindle ereader nook 1. Students love it. Students love using new technologies in the classroom. Whether it be the "cool" factor or just a genuine interest in new technology, it allows teachers to vary activities of the day and engage students. There is no commandment for education that says, "THOU SHALT NOT HAVE FUN" Students spend hours using technology like computers and television when they get home from school--why not engage them through their favorite mediums? 2. It engages the Four Key Components to Learning. Click on the link to read how technology in education (specifically the iPod School Listening Center) engages the 4 key components to learning. 3. Professional Development. Using new technologies allow students to learn another skill ...
	5.50 min. | 4.84 user rating
Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning Bay Area Vision Meeting (more info below) Unsupervised Feature Learning and Deep Learning Presented by Andrew Ng March 7, 2011 ABSTRACT Despite machine learning's numerous successes, applying machine learning to a new problem usually means spending a long time hand-designing the input representation for that specific problem. This is true for applications in vision, audio, text/NLP, and other problems. To address this, researchers have recently developed "unsupervised feature learning" and "deep learning" algorithms that can automatically learn feature representations from unlabeled data, thus bypassing much of this time-consuming engineering. Building on such ideas as sparse coding and deep belief networks, these algorithms can exploit large amounts of unlabeled data (which is cheap and easy to obtain) to learn a good feature representation. . These methods have also surpassed the previous state-of-the-art on a number of problems in vision, audio, and text. In this talk, I describe some of the key ideas behind unsupervised feature learning and deep learning, describe a few algorithms, and present case studies pertaining. The Bay Area Vision Meeting (BAVM) is an informal gathering (without a printed proceedings) of academic and industry researchers with interest in computer vision and related areas. The goal is to build community among vision researchers in the San Francisco Bay Area, however, visitors and travelers from afar are also encouraged to attend and present. New ...
	48.17 min. | 4.9508195 user rating

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