It's a simple misconception that Photoshop is a difficult tool to learn. In reality, learning the program is rather straightforward, and Adobe even provides a tutorial guide for new users. You can find this guide at ` You can learn much about Photoshop from various online tutorials, including the free online tutorials provided by the Adobe website (`www.adobe.com/products/photoshop.html`), and many colleges have Photoshop tutorials and training available for tuition fees. Use free online tools such as ` to learn Photoshop or a more specific aspect of the program.
Photoshop CS6, Adobe Photoshop 2015, Photoshop CC and Photoshop Elements 11 all require Windows 7 or higher or Mac OS X version 10.8 or higher. All the instructions below have been tested on macOS Sierra and Windows 7 versions, but the Mac version of Elements has not been tested and is not supported. How to install Photoshop Elements on macOS Photoshop Elements for Mac is a free download for macOS. To install it, download it from the official website, then double-click the downloaded Photoshop Elements icon, and sign in using your Mac (if you’re logged in). It’s likely that your Mac will connect to the internet to download and install the software and update it when you’re offline. Step 1: Open the Photoshop Elements installer file. Open the downloaded package file or drag the Adobe Photoshop Elements file onto your Mac using Finder. Alternatively, you can navigate to it in the Downloads folder. Step 2: Follow the on-screen instructions to install Photoshop Elements. With the package open, double-click the Photoshop Elements icon. This should automatically start the Photoshop Elements installer. Step 3: Follow the on-screen instructions to install Photoshop Elements. The first thing that appears is the installer window. Photoshop Elements allows you to either install the software itself (recommended) or just upgrade it. Step 4: Perform a simple, quick installation. The first thing that will appear is a page that explains that you need an internet connection. Click Next and confirm the restart option when prompted. Step 5: Install Photoshop Elements. Photoshop Elements will begin installing itself on your Mac. You can follow the progress by keeping an eye on the installer window. You will soon be presented with the setup screen. Click Next. Step 6: Choose your installation options. Make sure that the custom installation is selected and that the language is English, and click Next. Step 7: Read your license agreement. Read the license agreement then click I Agree to agree to the terms of the agreement. Step 8: Install Photoshop Elements. Photoshop Elements will begin installing itself on your Mac. You can follow the progress by keeping an eye on the installer window. Once it’s finished, the Photoshop Elements window will reappear. Click Finish. Step 9: Restart your Mac. You’re done! 05a79cecff
Q: svm: choose sigma value from data I have hundreds of data sets of different sizes (each set is in 300 dimensions). I have a set of sigma values that are "valid" according to the svm paper (e.g. [-1.0, 1.0] or the same thing with a fixed range). Now I want to choose an sigma value for my data (each set) from the valid ones. I know I can do this by finding the best possible value for the sigma (this is pretty easy to do with scipy) but how do I choose the value for that best possible sigma? I have some ideas (most of them come from other posts like this) I was thinking of picking the sigma value with the lowest F1_score I was thinking of pick one with the highest prediction accuracy does anyone have ideas how to do that and what to use for that? A: No, there is no magic answer to that. That's a point which is not a valid argument of introducing a cutoff or estimator to make the problem easier. You do have to test the whole spectrum of possible sigma values for each input data. If you use the lowest F1_score, you will have a problem if the feature space is very different from the original. Let's take the ideal case. You have a multidimensional feature space with perfect separability of your data, and only one non-linear decision boundary. That means you're actually dealing with a univariate problem. Now suppose you have only one data point in your training set (the cut-off for that point is 0). It will always have the highest F1_score when sigma = 0, which is obviously impossible. So if you always pick the sigma for which you have the highest F1_score, you'll pick a sigma value which is smaller than the real one for your data. If you want to pick a sigma value for your data that best explains your data, you have to first learn that from your data, and then learn that again for the validation data. You can use an SVM for learning, but I would suggest to use a Kernelized version (support vector regression) like scikit-learn. A: This problem belongs to the large family of learning-to-rank problems. I think for learning to
Q: Android - how to open an url and go to the actual website I'm trying to make an android application that when you click on a button, that it will open in a new tab a url (right now it opens it inside the device browser), also I want it to redirect the user to the url inside the tab that was opened (so how do I close the current tab?) Example: btn = (Button) findViewById(R.id.button); btn.setOnClickListener(new OnClickListener() { public void onClick(View arg0) { Intent browser = new Intent(Intent.ACTION_VIEW, Uri.parse("")); startActivity(browser); } }); A: Here's an example how to open browser with google search on it public class MainActivity extends Activity { @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); Button btn = (Button) findViewById(R.id.button); btn.setOnClickListener(new OnClickListener() { public void onClick(View arg0) { Intent browser = new Intent(Intent.ACTION_VIEW, Uri.parse("" + getIntent().getStringExtra("search"))); startActivity(browser); } }); } } And then in activity you must pass some value public class IndexActivity extends Activity { @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout
Minimum: OS: Windows 10, Windows 8.1, Windows 8, Windows 7 SP1, Windows Vista SP2 (SP1 or SP3) Windows 10, Windows 8.1, Windows 8, Windows 7 SP1, Windows Vista SP2 (SP1 or SP3) CPU: 2.4 GHz Intel Core 2 Duo or AMD Phenom II X2 (or better) 2.4 GHz Intel Core 2 Duo or AMD Phenom II X2 (or better) RAM: 1 GB 1 GB HDD: 7 GB
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