苹果发布新款iPad Pro, MacBook Air 和 Mac Mini

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中文翻译系博主自己翻译,转载请注明出处

Apple unveils new iPad Pro, MacBook Air and Mac Mini

苹果发布新款iPad Pro, MacBook Air 和 Mac Mini

By Samantha Murphy Kelly

翻译:https://blog.youkuaiyun.com/CodingIsFun

Updated 1941 GMT (0341 HKT) October 30, 2018

 

New York (CNN)Apple has brought its fanfare to Brooklyn.

纽约(CNN)苹果公司把果粉们带到了布鲁克林。

CEO Tim Cook kicked off his company's second keynote in two months on Tuesday. This one, held at the Brooklyn Academy of Music, follows its splashier September press event where it unveiled the iPhone XS, XS Max and XR.

继9月份闪耀发布iPhone XS, XS Max 和 XR之后,库克于本周二发表了2月之内的第二个主题演讲,这次的地点选在了布鲁克林音乐学院。

Apple unveiled what it's calling the biggest change to the iPad since its inception eight years ago. Its new features play in to Apple's mission to make the tablet more adaptable and appealing to a larger audience.

The new iPad Pro is 5.9 mm thinner — about 15% slimmer than its predecessor — and no longer features a headphone jack or Home button. You'll need to swipe certain spots on the screen to get back to previous pages. The iPad Pro also features nearly a edge-to edge-display and rounded corners.

苹果称这次发布的iPad是其iPad诞生8年以来的最为重大的一次改变。新款产品符合苹果一贯的作风,让平板功能更多样化,更迎合消费者的需求。新款iPad Pro厚5.9毫米-相比上一代产品大概薄15%-并且取消了耳机插孔和主屏幕按钮。用户需要滑动屏幕上的特定区域来返回上一页。新款iPad Pro还配有近乎全面屏的圆角屏幕。

The new iPad Pro

配图为新款iPad Pro

The iPad Pro also supports Face ID and USB-C, so it can connect to monitors up to 5K to turn into a workstation. It can even charge your iPhone, essentially turning it into your own personal power bank.

iPad Pro还支持Face ID和USB-C,可以连接到5K的显示器,作为工作站使用。另外还可以给iPhone充电,当充电宝使用。

The iPad Pro's Pencil magnetically connects to the side of the device and automatically charges — a small touch Apple hopes will resonate with creative users.

iPad Pro 配备的触控笔可以吸附到设备侧边,自动充电-苹果希望这点小小的改进能获得客户的认可。

It's available in two sizes: 11 inches starting at $799, and 12.9-inchfes starting at $999.

可选尺寸有两个:11英寸的起售价为799美元,12.9英寸的起售价为999美元。

Apple also showed off a completely redesigned MacBook Air, starting at $1199. That's up $200 from the previous model due largely to its new Retina display.

苹果还展示了一款重新设计的MacBook Air,起售价为1199美元。这个价格相比上一代产品贵了200美金,主要是因为其配备了新款视网膜屏幕。

"When Steve [Jobs] pulled that MacBook Air out of that envelope, it was clear things would never be the same," Cook told attendees. "The MacBook Air's incredibly thin design not only influenced the Mac line... it changed the industry."

库克在发布会现场对观众说“乔布斯从信封里拿出MacBook Air的那一刻,标志着新时代的到来”。“ MacBook Air纤薄的设计不仅影响到了整个Mac产品线,也改变了整个行业。”

The new MacBook Air.

配图为新款MacBook Air.

Cook promises those changes will continue. Apple said the MacBook Air is 25% lighter than its previous reiteration, now weighing in at 2.75 pounds. The familiar aluminum bezel has been removed for a sleeker border. For the first time, its base is made out of 100% recycled aluminum to help reduce the computer's carbon footprint by 50%, according to Apple.

库克承诺这些改进会持续下去。苹果称MacBook Air重量减轻了25%,现在重2.75磅。熟悉的铝合金切边被犀利的边框取代。并且,有史以来第一次采用100%回收的铝合金制造基座,并称公司的碳排放量减少了50%。

Beyond the company's effort to make it more eco-friendly, the 13.3-inch notebook features a more responsive keyboard. But noticeably absent from the device is the TouchBar feature introduced two years ago that brought a collection of controls right to the keyboard, and which many users disliked.

苹果公司除了在环保方面用力以外,给这次的13.3英寸的笔记本配备了响应更灵敏的键盘。但是有一点值得注意的是这台笔记本并没有配备2年前引入的TouchBar,该功能把一些控制功能集合到了键盘上,但是很多用户并不喜欢这一功能。

The MacBook Air now has Touch ID.

MacBook Air现在配备了Touch ID.

Apple also touted its new addition of Touch ID for increased security, but that inclusion is an interesting choice: Apple is moving away from the feature on its flagship iPhones in favor of Face ID.

苹果还重点强调了一下新加入的Touch ID这一特性提升了安全性,加入Touch ID这一选择耐人寻味:毕竟,苹果在其旗舰手机上已经逐渐在远离这一特性,而更倾向于Face ID.

The company also showed off its first update to the Mac Mini in four years. Although it looks strikingly similar to the last model, the overhauled device ($799) comes with a quad-core Intel processor with an option to upgrade to a six-core version. It features four USB-C Thunderbolt 3 ports and HDMI and Apple says it is five times faster. It now comes in space gray.

苹果公司还发布了新款Mac Mini,这是4年以来的首次更新。尽管产品看上去与上一代很相似,针对该产品(报价799美元)的重要更新包括加入一个4核的英特尔处理器,并且用户可以选择升级成6核。该产品具备4个USB-C 雷电3 接口,HDMI接口,苹果称该产品的运行速度是上一代的5倍。另外加入了深空灰颜色。

Apple's lineup of new devices.

苹果新款产品

In addition, Cook announced that iOS users can now upgrade to iOS 12.1 for more than 70 new emoji and the much-anticipated new group FaceTime feature.

另外,库克宣布iOS用户现在可以升级到12.1以获得70多个表情,另外加入了备受期待的FaceTime群聊功能。

Loup Ventures analyst Gene Munster said the announcements were mostly expected and "incrementally positive."

Loup Ventures分析师Gene Munster称,这次的发布会与预期相符,释放的信息很积极。

"We see today's iPad Pro updates as an indication of the blurring the lines between the iPad, the Mac, and the iPhone," Muenster wrote in a note. "Adding tech from the iPhone like FaceID, along with full Adobe apps, Xbox-level graphics, and a USB-C port that we usually associate with a Mac makes its 'tablet' categorization more ambiguous. This may increase Apple's addressable market by effectively creating a lower entry point for a full-fledged 'computer.'"

Muenster在其文章中指出“今天看到的iPad Pro的升级预示着iPad, Mac和iPhone的产品定位正在融合”。“加入iPhone中的FaceID, 以及功能齐全的Adobe软件,Xbox级别的动画渲染能力,和通常与Mac相关联的USB-C接口,这些功能让‘平板’的概念越来越模糊。此举或能降低接触苹果‘电脑’的门槛,扩大其市场份额。”

Meanwhile, ABI Research director David McQueen said Apple's move to add higher-end specs to its products will help it accommodate future technological advances, like 5G. McQueen said he believes brand loyalty will push customers to pay the higher prices.

同时,ABI Research负责人David McQueen称苹果增强其产品性能的举措能帮助其适应未来科技的发展,例如5G. McQueen称他相信对品牌的忠诚度会促使顾客付出更高的价格。

"Consumers are still happy to pay the price to have an Apple product having probably invested heavily already into the ecosystem," McQueen told CNN Business.

McQueen 对CNN Business称 “对于对苹果产品投入了相当多的顾客而言,他们仍然很乐意付钱购买苹果产品”。

Pre-orders start today and the new products hit stores next week.

预订自今天开始,新品从下周起会上架。

英文原文链接:https://edition.cnn.com/2018/10/30/tech/apple-event-october-2018/index.html

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Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); 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