Recovery The Hard Way

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When I look back at photos from when I was drinking, I realize just how bloated and yellow my skin looked. I even wound up in the emergency room a number of times. And all this was over the course of just two years. But denial can be powerful. Admitting that I may have a problem with alcohol meant that I would likely need to stop drinking, which I was unwilling to do.

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So instead of confronting my feelings about my drinking, I just shoved them further and further down. There were a few occasions that friends tried to talk to me about my relationship with alcoho l and I would admit that I tended to get out of control, then apologize, then return to drinking the same way.

I was eventually forced to confront my drinking, my demons and myself, despite how hard I tried to avoid doing just that. Sure, when it came to getting sober I had friends and family who pushed me in that direction and supported me along the way. But until I came to terms with alcoholism and admitted that drinking was a problem for me, their efforts did no good.

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I had to be the one to surrender and decide that I deserved a better life than the one I was leading while drinking. The only person who can truly control your actions and choices is yourself, and that goes for all aspects of life. Contact Us. Teen Addiction. What is Long Term Rehab? What to Pack for Rehab? Why is Accreditation Important? Don't wait another day. Help is a phone call away. Share By Beth Leipholtz. People will eventually stop giving chances I screwed up often when I was drunk.

You are the only person who can save yourself Sure, when it came to getting sober I had friends and family who pushed me in that direction and supported me along the way. MNIST digits may be irregularly shaped, but the images they live in are regular in important ways. The contrast and the brightness have been normalised so that pixels range in intensity from totally dark 00 to totally bright FF. Even more importantly: the digits are centred.

To maximise my odds of perfect classification, I need to convert my video files into very regular, tiny images of four-digit clusters like the ones shown above. Here are my steps, listed in part as commands I ran from a bash shell on my Linux laptop:. I begin with each of the movies I recorded sitting in its own subdirectory.

That is, I'm assuming this arrangement:. This step produces cropped images like the multi-digit "mess" shown just above. Note the conversion from colour to greyscale. The cropping rectangle the argument to the -crop flag was identified by hand. The beginning and ending of each movie have extra frames that were recorded while I moved my hands between the camera and the 's keyboard. These are not of much use. This step does most of the work. From the coarsely-cropped images in step 2, a Python program uses a crude gradient descent optimisation procedure to gradually adjust the locations of tiny cropping windows placed around each of the 4-digit words.

The technique sounds fancier than it is. The objective is to centre the windows on the words as precisely as possiblein fact, with subpixel precision that is, we'd like to specify the locations of the windows in fractional pixels. To do this, we iterate the following procedure for each window:. Although effective, this procedure isn't perfect.

For starters, it takes a long time: on my slightly-old laptop, the better part of a day to crop all 1,, word-images in the coarse crops from step 3. A more important issue comes from the "centre of gravity" strategy in the first place: some digits like 8 or B have more "on" pixels than others like 0 or 1, and so B cropped will not quite line up with B :.

In the end I chose to move on and hope for the best. If I were starting over from scratch, I might try a different technique that makes use of the morphological operators and connected components-based methods I used in recovering the 's executable ROS. You can see this banding for yourself at the YouTube link from earlier. Training data is the only essential ingredient for successful machine learning. All other details are basically fashion decisions. For a classifier like the one I want to make, training data amounts to pictures of 4-digit words the input to the classifier paired with the correct numerical label the four-digit output we'd like the classifier to produce.

For example:. With loads of training data, you can recognise anything simply: just scan all of your pairs like an enormous identity parade police lineup and emit the label associated with the closest match. This turns out to be a real machine learning technique. Unless all of these singletons wind up in the training data, they won't be identifiable via simple matching. More sophisticated classification methods learn to generalise from training examples in sophisticated ways, allowing them to decode input that they've never seen before.

This is most of the learning in "machine learning". The best techniques are distinguished by how much generalisation they can do.

One man’s tale of the hard road to recovery

So, if I use a good technique, how much training data will I need? It's difficult to know. Hoping that I won't need such an extreme ratio, and knowing that my dataset isn't MNIST, I choose an arbitrary minimum number of training data examples: , I've got some good friends who've helped me out on various projects over the years, but I think I'm on my own for the tedious task of labelling all of this training data. The least I can do is try to make it less painful for myself.

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Jotting down labels into a text editor would be pretty slow; instead, I decide to make a custom program that presents word images and collects digits as fast as I can type them. It looks like this:. This is an ordinary command-line program with interspersed graphics, made possible by using a terminal emulator that supports Sixel graphics.

This method for showing screen images inline with text was introduced in the early s by Digital to provide graphics capabilities for their VT and VT terminals. From the VT owner's manual. Since I don't have one of those, I'll use mlterm on my laptop instead. The program itself comes in two parts: the interface and a database backend library where the "database" is really just an enormous.

Both are written in ordinary Python, but the interface makes use of the UNIX-only termios and tty modules. Windows users might be able to get it working under cygwin. Here is the hastily-written! To use this program, you'll need an empty database first. Assuming you've followed all of the steps so far, this one , uncompressed, will work for you.

Beware: it's over 54 MiB, but there's a lot of duplicated text, so lzip compresses it down to a measly KiB.

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You will need to run the "labelthon" program from a directory where all of the paths in the database are valid, like so:. Now you and mlterm are ready to sit down for days and days of hand-labelling. MOV the second filming of the same thing are , so the --mark-apl-ros-czeros flag labels all of the images from those movies accordingly. Human error is a problem, so the program will want to collect two identical labellings from you before it believes the label you've chosen for a particular word image.

At a minimum, then, you'll need to type in , labels, although due to the way "labelthon" chooses between showing you a new image or asking you to verify an old one controllable by flags , you're likely to do even more than that. If you see a "gibberish" image like the one above, type m to mark it as nonsense.


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If the image is clearly many word images are, even after applying --mark-apl-ros-czeros , the shortcut z saves you from typing four digits. If you get tired of this madness, type q to quit. On New Years Day, I set to work:. Blue marks show times when I'm working the database library saves timestamped backups of the database in the background.

The minor horizontal ticks are hours. The count goes down on the 4th and the 5th because I change the flags to force "verification mode" and wind up clearing out errors. Fair enoughif I don't like the job so much, why inflict it on others? Yes, I've had a logic analyser this whole time. The graph above actually shows a zoom of the overall labelling progress, which looks more like this:.

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Fed up with labelling by the 7th, I'm off doing other things for a while, but before long I decide that I ought to save the 's executable ROS the other important ROS in the computer. This is a different project described elsewhere , but suffice it to say that it means opening up the again after I'd hoped to keep the covers shut. That recovery was fairly straightforward, and I might have been wise to adapt those techniques to the non-executable ROS, but then again, I'd already gone to so much trouble for the existing approach.

In any case, with the covers off, it's not hard to attach my logic analyser to the appropriate bus inside the and re-run the program that loads non-executable ROS data into memory. Surely with these traces I can easily reconstruct the entire non-executable ROS and forget all about machine learning, right? So, I make my recordings, gently untangle the logic probes from the 's circuitry, and close its case again, hopefully now for months or even years. A look at the data afterwards reveals two issues:.