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Why Does The UK Want To Leave The EU

By 2017, the United Kingdom is expected tohold a referendum to decide whether or not to remain a member of the European Union.The UK has been a part of the EU since 1973, and decided to remain a memberstate aftera referendum in 1975. So, why does the UK want to leave the EUé Well, Prime Minister David Cameron has saidthat the EU imposes too many restrictions on British lawmakers. Critics say that theUK will be forced to abandon its Pound currency and adopt the Euro currency, which has historicallybeen the weaker of the two. Adoption of the Euro is expected of all EU countries by theyear 2020. But many are worried that the Euro

is not only unstable, but able to be influencedby weaker countries like Greece. The UK is also concerned about European restrictionsimposed on their immigration laws. The EU currently provides the ability for migrantsto access employment and benefits. However, the British Prime Minister has been workingto restrict benefits and housing to those who have been in the country for at leastfour years. This proposal has been directly opposed by the European Commission, leadingmany Britons to question why the EU has so much say in British welfare. Some conservative groups feel like Europeis manipulating the British government, and

that policymakers have ‘lost control overtrade, human rights and migration'. EU law is supreme over UK law, meaning that lawmakersin the British House of Commons are technically beholden to those in the European Parliamentin Brussels. AntiEU activists believe that European regulations will undermine Britishinterests, which to many are isolationist. So how would a British exit affect both theEU and the UKé Well, analysts say that it could join Norway, Iceland and Liechtensteinin direct single market access. This would be enacted per the existing Agreement on theEuropean Economic Area. Many trade regulations would remain the same, without imposing thesame export and import tariffs as nonEU countries.

However, it wouldn't help the UK's issueswith free movement of labor, which is still guaranteed in the EEA Agreement. It wouldalso slow investment in the UK, which would no longer be seen as an entryway into EU trade.Similarly, the EU would lose some of its clout, as one of it's largest economic and politicalheavyweights goes off on its own. But all of this is based on who you ask. Atthe moment, the EU and UK are in talks to resolve their differences, but if they can'tcome to a solution, the scheduled referendum will likely occur. Overall, the United Kingdomhasn't made up its mind on the EU yet, and it'll take the upcoming referendum to solvethe question once and for all.

Greece has also threatened to leave the Eurozone,raising the question of whether being an EU member is even worth it. Learn more aboutthe EU's struggles in our tutorial. Thanks to your help, TestTube News is SO close tohitting one million subscribers, so make sure to like and subscribe for new tutorials everyday.

Convolutional Neural Networks Ep 8 Deep Learning SIMPLIFIED

If there's one deep net that has completelydominated the machine vision space in recent years, it's certainly the convolutionalneural net, or CNN. These nets are so influential that they've made Deep Learning one of thehottest topics in AI today. But they can be tricky to understand, so let's take a closerlook and see how they work. CNNs were pioneered by Yann Lecun of New YorkUniversity, who also serves as the director of Facebook's AI group. It is currently believedthat Facebook uses a CNN for its facial recognition software. A convolutional net has been the go to solutionfor machine vision projects in the last few

years. Early in 2015, after a series of breakthroughsby Microsoft, Google, and Baidu, a machine was able to beat a human at an object recognitionchallenge for the first time in the history of AI. It's hard to mention a CNN without touchingon the ImageNet challenge. ImageNet is a project that was inspired by the growing need forhighquality data in the image processing space. Every year, the top Deep Learning teamsin the world compete with each other to create the best possible object recognition software.Going back to 2012 when Geoff Hinton's team took first place in the challenge, every singlewinner has used a convolutional net as their

model. This isn't surprising, since theerror rate of image detection tasks has dropped significantly with CNNs, as seen in this image. Have you ever struggled while trying to learnabout CNNsé If so, please comment and share your experiences. We'll keep our discussion of CNNs high level,but if you're inclined to learn about the math, be sure to check out Andrej Karpathy'samazing CS231n course notes on these nets. There are many component layers to a CNN,and we will explain them one at a time. Let's start with an analogy that will help describethe first component, which is the “convolutional

layer� Imagine that we have a wall, which will representa digital image. Also imagine that we have a series of flashlights shining at the wall,creating a group of overlapping circles. The purpose of these flashlights is to seek outa certain pattern in the image, like an edge or a color contrast for example. Each flashlightlooks for the exact same pattern as all the others, but they all search in a differentsection of the image, defined by the fixed region created by the circle of light. Whencombined together, the flashlights form what's a called a filter. A filter is able to determineif the given pattern occurs in the image,

and in what regions. What you see in thisexample is an 8x6 grid of lights, which is all considered to be one filter. Now let's take a look from the top. In practice,flashlights from multiple different filters will all be shining at the same spots in parallel,simultaneously detecting a wide array of patterns. In this example, we have four filters allshining at the wall, all looking for a different pattern. So this particular convolutionallayer is an 8x6x4, 3dimensionsal grid of these flashlights. Now let's connect the dots of our explanation: Why is it called a convolutional neté The

net uses the technical operation of convolutionto search for a particular pattern. While the exact definition of convolution is beyondthe scope of this tutorial, to keep things simple, just think of it as the process of filteringthrough the image for a specific pattern. Although one important note is that the weightsand biases of this layer affect how this operation is performed: tweaking these numbers impactsthe effectiveness of the filtering process. Each flashlight represents a neuron in theCNN. Typically, neurons in a layer activate or fire. On the other hand, in the convolutionallayer, neurons perform this “convolution� operation. We're going to draw a box aroundone set of flashlights to make things look

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