* _Lifehacker's Guide to Photoshop_ has user-friendly tutorials, and the book's free download includes multiple Photoshop chapters from a variety of people. * _Photoshop CS3 for Mac and Windows QuickStart Guide_ has a tutorial for a user-friendly introduction to Photoshop that covers everything from getting started to editing a picture of a dog. * _Photoshop CS3: A Digital Photography Primer_ has eight chapters that detail the ins and outs of each of the Photoshop features. * _Hands On Photoshop_ has seven short tutorials that cover different aspects of photo editing, all bundled together in one download. * _Adobe Photoshop CS3_ _101: A Beginner's Guide to Adobe Photoshop_ has five chapters that introduce users to the basics of Photoshop. The book is a fairly inexpensive way to learn the basics without spending much money. * _Image Editing_ _101: Photoshop (CS3)_ covers the basics of Photoshop and has three useful chapters. * _Photoshop for Mac_ _Users_ _Unlimited: Essential Skills_ has an in-depth look at Photoshop and how to use it. It also has four chapters on such editing topics as adjusting levels, filtering, and using Levels and Curves. * _Photoshop_ _20/20_ _: The Fundamentals of Digital Photo_ _Editing_ has over 90 pages of photography tips and tricks. Photo editing isn't just for photographers, though. Many people use it for creating professional-level illustrations, designs, and more. For a quick, easy introduction to Photoshop, I recommend the free-to-download _Photoshop: Essential_ _Photoshop_ _CS3: Digital_ _Photography_ _Primer_ by Adobe Press.
Adobe Photoshop Elements Adobe has a free demo version, it can be downloaded from www.adobe.com/products/photoshop.html. It is easy to register for a full license. In this tutorial, we’ll cover common tasks that any Photoshop user can perform, a few tricks that you might not learn in a normal class. Begin by creating a new document. From the File menu select New. The first step is selecting the type of document you’d like to open in. If you’re opening a new document using the File menu, select the right type of document from the Types of documents category. Click Next. After clicking Next, you should see the New dialog. Leave the title as your file name, and the resolution as 8192×8192 if you’re creating a normal JPEG image. This will allow you to open the file in any Web browser without the need for resizing. If you wish to change the resolution of the image, see the section on Resizing images later in this tutorial. Click on the Create New button, and you should see the New dialog after a second. Enter in a filename and give the image a file size of around 1GB. Once you’ve got your file dimensions and resolution, it’s time to create a new document! Click on File > Open. The Open dialog will open. Select and open your image. Once the image has been selected, click OK, then under the File menu, select Save As. The Save As dialog will open. The filename should be your document name. Also, click in the Save As dialog and change the Save as Type to Adobe Photoshop Elements 11 (PSE) and name your image PhotoshopElements.psd. Click OK in the Save As dialog. The Photoshop Elements Editor will open, and you’ll be editing your new file! To exit, click File > Close. Resizing images You can resize images to a variety of sizes without having to change the dimensions of the canvas. Photoshop Elements 9 has limited support for resizing images: There is a resize tool box that can only be activated from the context menu (right click on the image). You can resize images to 200%, 150%, 125% or 100% of the original image size. To further increase the quality of the image, you can also resize images to 35%, 50%, a681f4349e
Q: Normalized weights of Dataset I was reading an explanation given on how to create a normalised weight for a dataset. In short the author says that, if the dataset can be represented as a vector then the weights will be inversely proportional to the length of the vector. The author has mentioned an example with hot questions. The vector length is proportional to the number of votes. I was a bit stumped. How does one derive a vector from a set of points? A: Just as the author said, the lengths of vectors are proportional to their values. If your data is $x_1, \dots, x_N$ which have length $N$, then the vector with the same coefficients but normalized to sum to one is $\frac{x_1}{N},\dots,\frac{x_N}{N}$. (For convenience this can be arranged so that it has $N$ entries, but it doesn't really matter. You can also normalize to have length one, which is just multiplying by the constant factor $\frac{1}{\sqrt{N}}$ in the denominator). In a dataset, if there's a vector $x_1,\dots,x_N$ which has length $N$ and represents the values for each data point, then what you are looking for is a vector $w_1,\dots,w_N$ (we can treat vectors as matrices for convenience) which represents the weights that you should use to multiply your data points. The point here is that the vector $w_1,\dots,w_N$ should have length $N$ just as the vector $x_1,\dots,x_N$. A very simple example is given by the list $1, 2, 3, 4, 5$ of lengths which are already normalized. What you're looking for is vectors $w_1,w_2,w_3,w_4,w_5$ which add up to $1$ but which represent the coefficients of the five weights. If you don't have weights to give to the data points, then this will be harder. Sometimes you have an explicit list of weights or importance (for example, so you can give different weights to questions which are hard vs. easy, or you might want to give higher weights to famous questions, or you
0 0 0 1 0 0 0 0 15 0 15 0 0 0 0 16 16 images\spr_mario_toad_1_0.png /******************************************************************************* * Copyright (c) 2018 IBM Corporation and others. * All rights reserved. This program and the accompanying materials * are made available under the terms of the Eclipse Public License v1.0 * which accompanies this distribution, and is available at * * * Contributors: * IBM Corporation - initial API and implementation *******************************************************************************/ package test.managed; import java.io.Serializable; import java.util.Arrays; import java.util.List; import java.util.stream.Collectors; import javax.persistence.Column; import javax.persistence.Entity; import javax.persistence.GeneratedValue; import jav
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