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appbar icon appbar

The app bar, also known as the action bar, is one of the most important design elements in your app's activities, because it provides a visual structure and interactive elements that are familiar to users. Using the app bar makes your app consistent with other Android apps, allowing users to quickly understand how to operate your app and have a great experience.

base icon base

Android项目快速集成基础框架

dagger icon dagger

A fast dependency injector for Android and Java.

deeplinks icon deeplinks

To enable Google to crawl your app content and allow users to enter your app from search results, you must add intent filters for the relevant activities in your app manifest. These intent filters allow deep linking to the content in any of your activities. For example, the user might click on a deep link to view a page within a shopping app that describes a product offering that the user is searching for.

encryption icon encryption

基于Base64,MD5,SHA,Hmac,DES,AES,RSA信息加密的设计与实现的Android应用。

ffmpeg icon ffmpeg

mirror of git://source.ffmpeg.org/ffmpeg.git

git icon git

Git Source Code Mirror - This is a publish-only repository and all pull requests are ignored. Please follow Documentation/SubmittingPatches procedure for any of your improvements.

helloopengl icon helloopengl

In order to draw graphics with OpenGL ES in your Android application, you must create a view container for them. One of the more straight-forward ways to do this is to implement both a GLSurfaceView and a GLSurfaceView.Renderer. A GLSurfaceView is a view container for graphics drawn with OpenGL and GLSurfaceView.Renderer controls what is drawn within that view. For more information about these classes, see the OpenGL ES developer guide.

image-classification icon image-classification

Image Classification Introduction In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset will need to be preprocessed, then train a convolutional neural network on all the samples. You'll normalize the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At then end, you'll see their predictions on the sample images.

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