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This device is a tablet computer but is able to turn into what’s essentially considered a notebook computer – or Wi-fi connected netbook, if you prefer. The keyboard dock here is reminiscent of the Transformer Prime and the original Eee Pad Transformer before that, and continues to be a pleasure to use like they were. The touchpad still needs tweaking if its going to have anywhere near as much quality as OS X does with the MacBook or the MacBook Air, but this has as much to do with the software – which really isn’t quite fully prepared for a desktop mode as such – as it is about the pad hardware itself.
If you plan on gaming on this machine with games that require you to have a mouse, I suggest finding a compatible USB mouse – which will plug in nicely as this units dock does indeed have a full-sized USB port. The dock itself also has a full-size SD card port and a port where you’ll be able to plug power into the wall or into your PC for desktop connectivity. The tablet then has a microHDMI port, a port for a microSD card, volume, power, and a headphone jack. ASUS doesn’t quote any special augmentation or especially fine quality parts for its headphone port, but it does sound perfectly fine for the basic media and gaming you’ll be experiencing with it.
There are two models per color, again we’ve got Royal Blue here in its 16GB configuration. There’s also a 16 and 32 GB configuration for each model, the Torch Red and Iceberg White models coming in June – pricing on all of these models is always $379 for the 16GB tablet, $399 for the 32GB model, and $149 for the keyboard dock. The Royal Blue version is expected to be delivered on the 30th of April with online retail availability starting the week of April 23rd.
According to ASUS, several hardware upgrades have happened since the Transformer TF101 including the camera and video capabilities of the unit. You’ve got an F2.2 aperture on this 8 megapixel camera and a slightly different camera interface out of the box to bring the quality up to a level that’s ready to take on the rest of the tablet industry easily. Head down to the Camera portion of this review to see the quality of this machine’s shooter. Also note that there’s no flash this time – so watch out for the dark!
Inside you’ll find Bluetooth 3.0, RAM at 1GB using new DDR3, and the tablet alone weighs in at 1.30 lbs. The dock weighs about half that, so expect about 2 pounds in your backpack with the full package. The tablet and keyboard together are just a bit thicker than the original Transformer Prime but are essentially the same dimensions otherwise – the tablet alone is 7.11 x 10.35 x 0.38-inches.
Aside from the slightly awkward nature of having the display half of your notebook being the heavier of its two halves, this product is extremely similar to the Transformer Prime. With the prime you’ve got metal, here you’ve got plastic. This unit isn’t completely made of plastic, of course, but big portions of its casing are – this for some will be a low point compared to the Prime as the Prime’s metal looks and feels high quality. For others though, the decrease in weight means an easier to handle unit.
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The display is also very similar in that it’s also an IPS technology-based panel, but here you’ve just got IPS and not IPS+, so you can’t burn your eyes out quite as easily here with this new model. This is another place where a feature has been cut to make way for a slightly lower price for the unit. Each of these small changes makes for a product that is still certainly high-end but just ever-so-slightly less impressive than the Prime. Have a peek at the hands-on video above to see both units side-by-side.
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Asus Transformer Pad Tf300T: Flashing Twrp Recovery 2.1.5
Owners of the ASUS Transformer TF300T are in for a special treat as the tablet is finally getting a custom recovery in the form of the TWRP (Team Win Recovery Project) Recovery 2.1.5. TWRP Recovery is a custom recovery built from the ground up, is easy to use, and features a lot of customization tools that users can benefit from.
Based primarily on the recovery images from the Android Open Source Project (AOSP), TWRP Recovery is loaded with all the standard recovery options but with added modifications such as a fully touch-driven interface to navigate instead of the traditional button pushing. The user-interface is fully coded in XML and is completely customizable by setting themes.
However, TWRP Recovery is currently a work in progress, with some minor issues that still need weeding out. Continue reading the rest of the guide to learn more on how you can install TWRP Recovery on your ASUS Transformer Pad TF300T.
Warning
The instructions in this guide are intended for use with the ASUS Transformer Pad, model number TF300T. Applying these instructions on another device or model may produce undesired outcomes.
The information in this guide is provided for instructional and educational purposes only. There is no guarantee that these instructions will work under your specific and unique circumstances.
Use these instructions at your own risk. We shall not hold any responsibility or liability for whatever happens to you or your device arising from your use of the info in this guide.
Read and understand the whole guide first before actually performing the instructions.
Requirements
ASUS Transformer Pad TF300T with unlocked bootloader
See our guide on how to unlock the bootloader of your ASUS Transformer Pad TF300T.
Fully charged tablet or at least 70% battery charge left.
Backup all personal data on your phone to make sure you have a copy of your personal data (e.g., contacts, SMS, MMS, Internet settings, Wi-Fi passwords, and the like) in case the procedure in this guide erases such data.
For backup tips, check our guides on how to sync your data to the cloud and how to create local backups of your mobile data.
Instructions
For this method, you’ll need a Windows PC, with the Android USB drivers installed on the PC. To install the drivers, just download and install the ASUS Pad PC Suite from the official ASUS support site for the tablet. Make sure that the PC Suite is not running, not even in the background, as you perform the steps below.
Download the following files and save them to your PC:
Fastboot (Fastboot.zip, 404.46 kB)
TWRP Recovery (openrecovery-twrp-2.1.5-tf300t.blob, md5sum: 184a0f164fba5abe22f6e2931a6a2924)
Extract the chúng tôi file and place the extracted files inside a single folder. You should acquire 4 files, namely:
adb.exe
AdbWinApi.dll
AdbWinUsbApi.dll
fastboot.exe
Rename the downloaded chúng tôi file into chúng tôi (for easier reference).
Copy or move the chúng tôi file to where chúng tôi is located. You should have a total of 5 files by now.
Switch off your tablet.
Boot into Fastboot Mode by pressing and holding down the Power and Volume Down buttons until you see the message “Press Vol Up to enter RCK”. Release the buttons when you see this message and wait for about 5 seconds. A new menu appears. Press the Volume Down button to select the USB icon, then press the Volume Up button to confirm your selection within 10 seconds, or else your tablet boots into Normal Mode.
Connect your tablet to your PC via USB cable.
Enter the following command at the command prompt to install TWRP Recovery.
fastboot -i 0x0B05 flash recovery twrp.blob
After installing, disconnect your tablet and reboot.
Terminal Emulator MethodThis procedure is an easy one to perform. You will need the Android Terminal Emulator app installed on your tablet for this method to work.
Download the TWRP Recovery file (openrecovery-twrp-2.1.5-tf300t.blob, md5sum: 184a0f164fba5abe22f6e2931a6a2924) from here and save it to your computer.
Rename the file into chúng tôi (for easier management).
Copy chúng tôi to your tablet’s internal SD card (i.e., /sdcard).
On your tablet, launch the Terminal Emulator app.
Type the following commands and press the Enter key after each line:
su
dd if=/sdcard/twrp.blob of/dev/block/mmcblk0p4
After the last command has been entered, restart your tablet to complete the installation.
Congratulations! You have successfully installed TWRP Recovery on your ASUS Transformer Pad TF300T. You can now install custom ROMs, hacks and kernels using the custom recovery.
Review: The Asus Et2702 Is A Good All
We dig the Haswell-class CPU and the powerful video card, but the absence of an SSD cache for the mechanical hard drive hurts the ET2702’s performance on productivity apps.
Full HD displays are so last season. Asus’s new ET2702 is the company’s first 27-inch all-in-one to sport a quad HD display—that is, a display with a resolution of 2560 by 1440 pixels, instead of the usual 1920 by 1080 pixels. That higher pixel density makes a big difference when you’re sitting merely 20 to 40 inches away from a 27-inch screen.
But there’s more to the Asus ET2702 than just its screen. Our review model, which costs around $1899 as configured (as of August 14, 2013), sports a quad-core Intel Core i7-4770 processor (a member of the new Haswell family), 8GB of DDR3/1600 memory, a discrete graphics card (AMD’s Radeon HD 8890A), a Blu-ray player, and a 2TB, 7200-rpm hard drive. In pricing, the ET2702 is on a par with comparable systems: It’s more expensive than the $1440 Vizio CA27T-B1, which has a slower processor and a lower-resolution display, but it’s about $200 cheaper than the Dell XPS 27 Touch, which has a lower-voltage version of the Core i7 processor (the Core i7-4770S).
Although the ET2702 boasts some impressive specs, it disappointed somewhat in benchmark performance. For instance, it earned a score of 174 in our Desktop WorldBench 8.1 benchmark tests. That’s good—it means the ET2702 is 75 percent faster than our baseline model, the Acer Aspire U—but it’s not fantastic, especially compared with the scores of other 27-inch all-in-ones we’ve reviewed recently. The Vizio CA27T-B1, which has an older third-generation Intel Core i7 processor, scored 179 on WorldBench 8.1. The Dell XPS 27 Touch, meanwhile, blew both the ET2702 and the CA27T-B1 out of the water with its mark of 262.
The absence of an SSD cache for the ET2702’s mechanical hard drive had a significant negative impact on this all-in-one’s Desktop WorldBench 8.1 score. Elements of our WorldBench suite that are based on performance in productivity applications, such as PCMark 7, suffered the most from the absence of an SSD cache.
Those results don’t mean that the ET2702 is a lousy computer. It actually delivers superior graphics compared to what Dell and Vizio are providing with their AIOs, and thus it is the best choice of the three if you’re looking to play games. Whereas Dell uses Nvidia’s GeForce GT 750M card, and Vizio relies on integrated graphics, Asus splurges on an AMD Radeon HD 8890A. In our Dirt Showdown test (resolution of 1024 by 768 pixels, with low visual-quality settings), the ET2702 managed an impressive frame rate of 131.7 frames per second. By comparison, Dell’s XPS 27 Touch delivered 125.7 fps and the Vizio CA27T-B1 chugged along at a rate of just 54.4 fps on the same test.
Gaming performance is a bright spot for the Asus ET2702, thanks to its Asus Radeon HD 8890A discrete video card.
The Achilles’ heel of the ET2702 is its storage subsystem: Dell and Vizio both include a 32GB solid-state drive as a cache for their machines’ hard drives, whereas Asus does not. That cache makes a tremendous difference in many applications that involve frequent data fetches from storage. As a result, it affects many of the benchmarks that comprise our WorldBench 8.1 suite. Asus does provide up to a 128GB SSD on some other versions of the ET2702, but not on the model the company sent us (specifically, its model ET2702IGTH-B023K).
ROBERT CARDINThe stand on the Asus ET2702 allows only a very limited tilt range.
Even though the ET2702 doesn’t have the best touchscreen display I’ve seen (that honor belongs to Dell’s XPS 27 Touch), it does deliver crisp, clear text and images, as well as bright and accurate colors. Touch is especially nice on the ET2702: Multitouch gestures are smooth and accurate, not at all choppy as I’ve seen on some touchscreens. I also appreciate the bezel-free, edge-to-edge glass design, which makes it much easier to perform Windows 8 gestures such as swiping from the side or the top of the screen to access menus. I also enjoyed watching HD streaming video on the ET2702’s screen, although I did notice visual artifacts and blurred details.
The rest of the ET2702 needs some work. This AIO looks appealing from far away, thanks to its sleek profile and brushed-metal accents, but Asus needs to make several tweaks for better usability. Four labels in the lower-right corner of the screen, for instance, identify touch-sensitive nubs below the screen for mode, volume up/down, menu, and brightness up/down. The nubs are confusing to use—I wasn’t sure whether I was supposed to swipe, tap, or press them. They’re also inconsistently sensitive, and generally they never did what I expected them to do. Tapping the volume nub, for example, changed the brightness.
The Asus ET2702 isn’t a bad all-in-one PC, especially if you’re a sucker for pretty screens, but it’s not the best. The Dell XPS 27 Touch is faster and more attractive, and it suffers from fewer usability issues. And Dell’s machine costs only $200 more.
How To Use Pytorch Pad With Examples?
Introduction to PyTorch Pad
The pyTorch pad is the function available in the torch library whose fully qualifies name containing classes and subclasses names is
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torch.nn.functional.pad (inputs, padding, mode = "constant", value = 0.0)It is used for assigning necessary padding to the tensor. In this article, we will try to dive into the topic of PyTorch padding and let ourselves know about PyTorch pad overviews, how to use PyTorch pad, PyTorch pad sequences, PyTorch pad Parameters, PyTorch pad example, and a Conclusion about the same.
PyTorch pad overviewsThe pyTorch pad is used for adding the extra padding to the sequences and the input tensors for the specified size so that the tensor can be used in neural network architecture. In the case of string values, the information is mostly provided in the natural language processing, which cannot be directly used as input to the neural network. For this, the padding is added. So that the batch can be maximized to the largest dimension value and cover the empty spaces of each patch with the padding value. Most of the time, the value of padding used is 0 (zero).
Also, it would help if you kept in mind that when you use the CUDA backend, the pad operation will add a completely non-deterministic behavior. This behavior can not be switched off easily. You can refer to this link for additional details about the background reproducibility.
How to use PyTorch pad?We can use the PyTorch pad by using the function definition specified above. Also, there are certain factors related to the padding that will help you to understand how padding will happen and how it can be used that are discussed here –
Size of padding – The padding size is the value by which we want a particular input of certain dimensions to pad. We can describe the padding size starting from the last dimension and moving further. For example, the input with dimensions of [length(padding)/2] will be padded. Let us take one example to understand its works; if you want to pad the input tensor’s last dimension, we can do so by specifying the form of the pad as (left padding, right padding). In the case of the last two dimensions of input, a tensor is to be padded; then, we can specify the padding in the form (left padding, right padding, top padding, bottom padding). Finally, for padding of the last three dimensions of the input tensor, we can specify the padding form (left padding, right padding, top padding, bottom padding, front padding, back padding).
Mode of padding – There are three padding modes: ReplicationPad2d, ReflectionPad2d, and ConstantPad2d. Reflection and replication padding is used for padding the last three dimensions of the tensor input, which is 5D size, while constant padding works for arbitrary dimensions. Reflection and replication also work when padding is done for the two final dimensions of the tensor input having a 4-dimensional size and even the single last dimension of the input tensor having a 3-dimensional size.
PyTorch pad sequencesMost of the sequences containing the text information have variable lengths. Therefore, when we use them in neural networks or architecture, we will have to add the padding for all the inputs you will provide as sequences. Usually, this padding added is 0s at the end of the batch just because the sequence length can be maximized to the length that fits all the data of the same batch.
For example, if we have the text data
It’s a beautiful day
Yes It is
Sure
Here, we can you this data for natural language processing, but in the case of neural networks, we will have to pad the input data at last by any value so that each of the batches maximizes to the length of a sequence of 4. After padding, our data will look like this –
It’s a beautiful day
Yes It is
Sure
PyTorch pad ParametersWe can make the use of pad function by using its syntax or definition of the function, which is –
torch.nn.functional.pad(inputs, padding, mode = "constant", value = 0.0)Various parameters used in the above function definition can be used by using the below-mentioned description –
Inputs: This object is of tensor form and has dimensions of n size.
Pad: It is a tuple value that consists of m elements. The size of m/2 is less than or equal to the specified input tensor’s dimension, and the value of m is always an even number.
Mode: This parameter can have a different value of mode that can be circular, reflect, replicate, and constant. By default, the value is considered constant when not specified.
Value: This is the padding value used for constant padding. By default, the considered value is 0 when not specified.
Examples of PyTorch padLet us understand the implementation of the pad function with the help of one example.
Example #1Code:
sample4DEducbaTensor = torch.empty(3, 3, 4, 2) paddingLastDimension = (1, 1) # for each side padding outputPaddedTensor = F.pad(sample4DEducbaTensor, paddingLastDimension, "constant", 0) # effectively zero padding print (outputPaddedTensor.size())Output:
Example #2 sample2DEducbaTensor = (1, 1, 2, 2) # padding for second last dimension by (2, 2) and last dimension by (1,1) outputPaddedTensor = F.pad(sample4DEducbaTensor, sample2DEducbaTensor, "constant", 0) print (outputPaddedTensor.size())Output:
Example #3Code:
sample4DEducbaTensor = torch.empty(3, 3, 4, 2) p3d = (0, 1, 2, 1, 3, 3) # padding for left, right, up, down, backward and front outputPaddedTensor = F.pad(sample4DEducbaTensor, p3d, "constant", 0) print (outputPaddedTensor.size())Output:
ConclusionThe pyTorch pad is used for adding the padding to the tensor so that it can be passed to the neural networks. By default, the value of padding is 0.
Recommended ArticlesWe hope that this EDUCBA information on “PyTorch Pad” was beneficial to you. You can view EDUCBA’s recommended articles for more information.
Asus Zenbook 17 Fold Oled Hands
The incredible shape-shifting Zenbook
Also read: The best laptops you can buy in 2023
Design and construction: mostly goodFor such a flexible device (pun intended), it was important for ASUS to get the design and the ergonomics right. It’s a bit of mixed bag, in my experience. The Zenbook 17 Book feels classy and reassuringly solid in the hand, especially when it’s folded up and closed. It looks a lot like a nice leather-bound organizer; I could see an executive whip it out during a boardroom meeting. It’s chunky and pretty hefty, at around 1.5kg, but not excessively so, considering the 17-inch screen it houses.
Ryan McLeod / Android Authority
While the build and design are generally on point, I did spot some flaws. For one, it’s not easy to quickly open and unfold the whole contraption. Or at least it wasn’t obvious and intuitive to me, in the brief time I spent with the device. The kickstand on the back doesn’t inspire confidence either. It’s small and flimsy-looking – it will do the job when using the screen on a desktop or other flat surface, but it won’t be very stable in a lap or on your couch.
The Zenbook 17 Book feels classy and reassuringly solid in the hand
The Bluetooth keyboard is generously sized or at least as big as you can expect from a 12-inch form factor. It attaches magnetically to the bottom of the tablet, but you can also use it as a standalone keyboard. It does suffer from a slight flexing issue when you place it on top of the lower half of the screen.
Ryan McLeod / Android Authority
Overall, ASUS has done a good job bringing the Zenbook 17 Fold up as a ready-to-market consumer product. Just remember this is a first-generation, second-of-its-kind device. It’s far from perfect, just like Samsung’s original Galaxy Fold was flawed in its first iteration.
Pricey potentialLooking beyond its shape-shifting abilities, the ASUS Zenbook 17 Fold OLED is a competent laptop, if not a stellar one. You get an Intel Core i7-1250U CPU, Intel Iris Xe integrated graphics, 1TB of storage, and 16GB of RAM. The performance is adequate for a content consumption laptop or office computer, but this is not a great choice for gaming or video editing. Battery life is surprisingly solid considering the size of the screen and the limited space inside, at about 10 hours. The port selection is meagre – you get two Thunderbolt 4 ports and a headphone jack.
Bogdan Petrovan / Android Authority
The ASUS Zenbook 17 Fold OLED is a pricey piece of equipment, starting at $3,500 in the US and €4,000 in Europe. That’s the massive price you’ll have to pay to be on the folding edge of computing, but is it worth it? Not really, at least not for most people. I am fairly sure that ASUS doesn’t care about that though, as the Zenbook 17 Fold is not a mass-market product. It’s other things: a road opener, a vision demonstrator, a statement of interest, and a shot across the bow towards the competition.
Zenbook 17 Fold OLED is a road opener, a vision demonstrator, a statement of interest, and a shot across the bow towards the competition.
The short time I spent with the ASUS Zenbook 17 Fold OLED has convinced me this form factor has immense potential. While the product itself makes a few too many sacrifices, especially for the huge price it commands, ASUS’s foldable laptop is undeniably cool. The ability to expand a tiny laptop into a beautiful big screen is compelling. All ASUS needs to do now is refine the idea over a few generations and bring the price down to earth.
Asus Radeon Hd 6990 Listed, Priced, Up For Pre
Since the official launch of the HD 6990 is rumored to happen no later than tomorrow, it shouldn’t come as much of a surprise that some of its implementations are already starting to show up, one of the first to appear online coming from Asus. As you might expect, we’re not talking about an official announcement here, since we’re still one day away from the official launch and the NDA is still in effect, but about somewhat of a slip-up, namely the device being listed for pre-order on a Dutch online shop called Salland. Of course, a short list of specs is also provided, but, as you might have suspected already, it pretty much falls in line with the rest of the leaks we’ve come across up until now, starting from the DirectX 11 support. So, according to the aforementioned source, the new GPU (well, twin GPUs, to be precise) will work at a frequency of 830 Mhz, while also coming equipped with no less than 4096 MB of GDDR5 memory, running at an impressive 5,000 Mhz. Furthermore, the device will be able to output a total resolution of 2560 x 1600 pixels, at the same time featuring a variety of connectivity options, including here one DVI port and no less than four mini DisplayPort connectors, while HDMI and D-Sub interfaces are also supported, via appropriate converters. Now, we were pretty sure that the HD 6990 wasn’t going to come cheap, and the guys over at Salland seem to confirm this, since they’re asking 715 Euro (1,000 US dollars) for the new card, which is, let’s face it, a fairly high price point for most users. However, since we’re not talking about an official announcement, we’ll simply have to wait for AMD’s official statement on the matter in order to start considering what exactly we’ll have to give up (or donate for medical research) in order to afford one such card.
Since the official launch of the HD 6990 is rumored to happen no later than tomorrow, it shouldn’t come as much of a surprise that some of its implementations are already starting to show up, one of the first to appear online coming from Asus. As you might expect, we’re not talking about an official announcement here, since we’re still one day away from the official launch and the NDA is still in effect, but about somewhat of a slip-up, namely the device being listed for pre-order on a Dutch online shop called Salland. Of course, a short list of specs is also provided, but, as you might have suspected already, it pretty much falls in line with the rest of the leaks we’ve come across up until now, starting from the DirectX 11 support. So, according to the aforementioned source, the new GPU (well, twin GPUs, to be precise) will work at a frequency of 830 Mhz, while also coming equipped with no less than 4096 MB of GDDR5 memory, running at an impressive 5,000 Mhz. Furthermore, the device will be able to output a total resolution of 2560 x 1600 pixels, at the same time featuring a variety of connectivity options, including here one DVI port and no less than four mini DisplayPort connectors, while HDMI and D-Sub interfaces are also supported, via appropriate converters. Now, we were pretty sure that the HD 6990 wasn’t going to come cheap, and the guys over at Salland seem to confirm this, since they’re asking 715 Euro (1,000 US dollars) for the new card, which is, let’s face it, a fairly high price point for most users. However, since we’re not talking about an official announcement, we’ll simply have to wait for AMD’s official statement on the matter in order to start considering what exactly we’ll have to give up (or donate for medical research) in order to afford one such card.
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