�ݺ�ߣshows by User: lekaha

�ݺ�ߣshows by User: lekaha / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: lekaha / Thu, 08 Feb 2018 07:29:33 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: lekaha Dagger for android /slideshow/dagger-for-android/87483801 daggerforandroid-180208072933
One of the central difficulties of writing an Android application using Dagger is that many Android framework classes are instantiated by the OS itself, like Activity and Fragment, but Dagger works best if it can create all the injected objects. Instead, you have to perform members injection in a lifecycle method. ]]>
One of the central difficulties of writing an Android application using Dagger is that many Android framework classes are instantiated by the OS itself, like Activity and Fragment, but Dagger works best if it can create all the injected objects. Instead, you have to perform members injection in a lifecycle method. ]]> Thu, 08 Feb 2018 07:29:33 GMT /slideshow/dagger-for-android/87483801 lekaha@slideshare.net(lekaha) Dagger for android lekaha One of the central difficulties of writing an Android application using Dagger is that many Android framework classes are instantiated by the OS itself, like Activity and Fragment, but Dagger works best if it can create all the injected objects. Instead, you have to perform members injection in a lifecycle method. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/daggerforandroid-180208072933-thumbnail.jpg?width=120&height=120&fit=bounds" /> One of the central difficulties of writing an Android application using Dagger is that many Android framework classes are instantiated by the OS itself, like Activity and Fragment, but Dagger works best if it can create all the injected objects. Instead, you have to perform members injection in a lifecycle method.

Dagger for android from Kan-Han (John) Lu

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0 Android develop guideline /slideshow/android-develop-guideline/76905602 androiddevelopguideline-170613151323
Android develop guideline: - Android develop environment - Android coding style - Workflow - Teamwork - Good practice]]>
Android develop guideline: - Android develop environment - Android coding style - Workflow - Teamwork - Good practice]]> Tue, 13 Jun 2017 15:13:23 GMT /slideshow/android-develop-guideline/76905602 lekaha@slideshare.net(lekaha) Android develop guideline lekaha Android develop guideline: - Android develop environment - Android coding style - Workflow - Teamwork - Good practice <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/androiddevelopguideline-170613151323-thumbnail.jpg?width=120&height=120&fit=bounds" /> Android develop guideline: - Android develop environment - Android coding style - Workflow - Teamwork - Good practice

Android develop guideline from Kan-Han (John) Lu

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0 Working process and git branch strategy /slideshow/working-process-and-git-branch-strategy/76905447 workingprocessandgitbranchstrategy1-170613150927
The sharing of experience of working process and working with Git.]]>
The sharing of experience of working process and working with Git.]]> Tue, 13 Jun 2017 15:09:26 GMT /slideshow/working-process-and-git-branch-strategy/76905447 lekaha@slideshare.net(lekaha) Working process and git branch strategy lekaha The sharing of experience of working process and working with Git. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/workingprocessandgitbranchstrategy1-170613150927-thumbnail.jpg?width=120&height=120&fit=bounds" /> The sharing of experience of working process and working with Git.

Working process and git branch strategy from Kan-Han (John) Lu

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0 Deep neural network for youtube recommendations /slideshow/deep-neural-network-for-youtube-recommendations/76905338 deepneuralnetworkforyoutuberecommendations-170613150558
The presentation of reading https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf]]>
The presentation of reading https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf]]> Tue, 13 Jun 2017 15:05:58 GMT /slideshow/deep-neural-network-for-youtube-recommendations/76905338 lekaha@slideshare.net(lekaha) Deep neural network for youtube recommendations lekaha The presentation of reading https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deepneuralnetworkforyoutuberecommendations-170613150558-thumbnail.jpg?width=120&height=120&fit=bounds" /> The presentation of reading https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf

Deep neural network for youtube recommendations from Kan-Han (John) Lu

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0 Android testing part i /slideshow/android-testing-part-i/76905227 androidtestingparti-170613150314
Android Local Unit Test]]>
Android Local Unit Test]]> Tue, 13 Jun 2017 15:03:13 GMT /slideshow/android-testing-part-i/76905227 lekaha@slideshare.net(lekaha) Android testing part i lekaha Android Local Unit Test <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/androidtestingparti-170613150314-thumbnail.jpg?width=120&height=120&fit=bounds" /> Android Local Unit Test

Android testing part i from Kan-Han (John) Lu

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0 Cuda project paper /lekaha/cuda-project-paper cudaprojectpaper-160325161937
Distributed Video Coding (DVC) has become increasingly popular in recent times among the researchers in video coding due to its attractive and promising features. DVC primarily has a modified complexity balance between the encoder and decoder, in contrast to conventional video codecs. However, Most of the reported DVC schemes have a high time-delay in decoder which hinders its practical application in real-time systems. In this work, we focus on speed up the Side Information(SI) generation module in DVC, which is a major function in the DVC coding algorithm and one of the time-consuming factor at the decoder. By applied it through Compute Unified Device Architecture (CUDA) based on General-Purpose Graphics Processing Unit (GPGPU), the experimental results show that a considerable speedup can be obtained by using the proposed parallelized SI generation algorithm.]]>
Distributed Video Coding (DVC) has become increasingly popular in recent times among the researchers in video coding due to its attractive and promising features. DVC primarily has a modified complexity balance between the encoder and decoder, in contrast to conventional video codecs. However, Most of the reported DVC schemes have a high time-delay in decoder which hinders its practical application in real-time systems. In this work, we focus on speed up the Side Information(SI) generation module in DVC, which is a major function in the DVC coding algorithm and one of the time-consuming factor at the decoder. By applied it through Compute Unified Device Architecture (CUDA) based on General-Purpose Graphics Processing Unit (GPGPU), the experimental results show that a considerable speedup can be obtained by using the proposed parallelized SI generation algorithm.]]> Fri, 25 Mar 2016 16:19:37 GMT /lekaha/cuda-project-paper lekaha@slideshare.net(lekaha) Cuda project paper lekaha Distributed Video Coding (DVC) has become increasingly popular in recent times among the researchers in video coding due to its attractive and promising features. DVC primarily has a modified complexity balance between the encoder and decoder, in contrast to conventional video codecs. However, Most of the reported DVC schemes have a high time-delay in decoder which hinders its practical application in real-time systems. In this work, we focus on speed up the Side Information(SI) generation module in DVC, which is a major function in the DVC coding algorithm and one of the time-consuming factor at the decoder. By applied it through Compute Unified Device Architecture (CUDA) based on General-Purpose Graphics Processing Unit (GPGPU), the experimental results show that a considerable speedup can be obtained by using the proposed parallelized SI generation algorithm. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cudaprojectpaper-160325161937-thumbnail.jpg?width=120&height=120&fit=bounds" /> Distributed Video Coding (DVC) has become increasingly popular in recent times among the researchers in video coding due to its attractive and promising features. DVC primarily has a modified complexity balance between the encoder and decoder, in contrast to conventional video codecs. However, Most of the reported DVC schemes have a high time-delay in decoder which hinders its practical application in real-time systems. In this work, we focus on speed up the Side Information(SI) generation module in DVC, which is a major function in the DVC coding algorithm and one of the time-consuming factor at the decoder. By applied it through Compute Unified Device Architecture (CUDA) based on General-Purpose Graphics Processing Unit (GPGPU), the experimental results show that a considerable speedup can be obtained by using the proposed parallelized SI generation algorithm.

Cuda project paper from Kan-Han (John) Lu

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0 Twitter as a personalizable information service ii /slideshow/twitter-as-a-personalizable-information-service-ii/60031026 twitterasapersonalizableinformationserviceii-160325161625
Twitter is a free social networking microblogging service that allows registered members to broadcast, in real-time, short posts called tweets. Twitter members can broadcast tweets and follow other users’ tweets by using multiple devices, making this information system one of the fastest in the world. In this chapter, we leverage this characteristic to introduce a novel topic-detection method aimed at informing, in real-time, a specific user about the most emerging arguments expressed by the network around his/her domain interests. With this goal, we aim at formalizing the information spread over the network by studying the topology of the network and by modeling the implicit and explicit connections among the users. Then, we propose an innovative term aging model, based on a biological metaphor, to retrieve the freshest arguments of discussion, represented through a minimal set of terms, expressed by the community within the foci of interest of a specific user. We finally test the proposed model through various experiments and user studies.]]>
Twitter is a free social networking microblogging service that allows registered members to broadcast, in real-time, short posts called tweets. Twitter members can broadcast tweets and follow other users’ tweets by using multiple devices, making this information system one of the fastest in the world. In this chapter, we leverage this characteristic to introduce a novel topic-detection method aimed at informing, in real-time, a specific user about the most emerging arguments expressed by the network around his/her domain interests. With this goal, we aim at formalizing the information spread over the network by studying the topology of the network and by modeling the implicit and explicit connections among the users. Then, we propose an innovative term aging model, based on a biological metaphor, to retrieve the freshest arguments of discussion, represented through a minimal set of terms, expressed by the community within the foci of interest of a specific user. We finally test the proposed model through various experiments and user studies.]]> Fri, 25 Mar 2016 16:16:25 GMT /slideshow/twitter-as-a-personalizable-information-service-ii/60031026 lekaha@slideshare.net(lekaha) Twitter as a personalizable information service ii lekaha Twitter is a free social networking microblogging service that allows registered members to broadcast, in real-time, short posts called tweets. Twitter members can broadcast tweets and follow other users’ tweets by using multiple devices, making this information system one of the fastest in the world. In this chapter, we leverage this characteristic to introduce a novel topic-detection method aimed at informing, in real-time, a specific user about the most emerging arguments expressed by the network around his/her domain interests. With this goal, we aim at formalizing the information spread over the network by studying the topology of the network and by modeling the implicit and explicit connections among the users. Then, we propose an innovative term aging model, based on a biological metaphor, to retrieve the freshest arguments of discussion, represented through a minimal set of terms, expressed by the community within the foci of interest of a specific user. We finally test the proposed model through various experiments and user studies. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/twitterasapersonalizableinformationserviceii-160325161625-thumbnail.jpg?width=120&height=120&fit=bounds" /> Twitter is a free social networking microblogging service that allows registered members to broadcast, in real-time, short posts called tweets. Twitter members can broadcast tweets and follow other users’ tweets by using multiple devices, making this information system one of the fastest in the world. In this chapter, we leverage this characteristic to introduce a novel topic-detection method aimed at informing, in real-time, a specific user about the most emerging arguments expressed by the network around his/her domain interests. With this goal, we aim at formalizing the information spread over the network by studying the topology of the network and by modeling the implicit and explicit connections among the users. Then, we propose an innovative term aging model, based on a biological metaphor, to retrieve the freshest arguments of discussion, represented through a minimal set of terms, expressed by the community within the foci of interest of a specific user. We finally test the proposed model through various experiments and user studies.

Twitter as a personalizable information service ii from Kan-Han (John) Lu

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0 Multimedia data minig and analytics sentiment analysis using social multimedia /slideshow/multimedia-data-minig-and-analytics-sentiment-analysis-using-social-multimedia/60030942 multimediadataminigandanalyticssentimentanalysisusingsocialmultimedia-160325161327
● The growing importance of sentiment analysis coincides with the popularity of social network platform (Facebook, Twitter, Flickr). ● A tremendous amount of data in different forms including text, image, and videos makes sentiment analysis a very challenging task. ● In this chapter, we will discuss some of the latest works on topics of sentiment analysis based on visual content and textual content.]]>
● The growing importance of sentiment analysis coincides with the popularity of social network platform (Facebook, Twitter, Flickr). ● A tremendous amount of data in different forms including text, image, and videos makes sentiment analysis a very challenging task. ● In this chapter, we will discuss some of the latest works on topics of sentiment analysis based on visual content and textual content.]]> Fri, 25 Mar 2016 16:13:27 GMT /slideshow/multimedia-data-minig-and-analytics-sentiment-analysis-using-social-multimedia/60030942 lekaha@slideshare.net(lekaha) Multimedia data minig and analytics sentiment analysis using social multimedia lekaha ● The growing importance of sentiment analysis coincides with the popularity of social network platform (Facebook, Twitter, Flickr). ● A tremendous amount of data in different forms including text, image, and videos makes sentiment analysis a very challenging task. ● In this chapter, we will discuss some of the latest works on topics of sentiment analysis based on visual content and textual content. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/multimediadataminigandanalyticssentimentanalysisusingsocialmultimedia-160325161327-thumbnail.jpg?width=120&height=120&fit=bounds" /> ● The growing importance of sentiment analysis coincides with the popularity of social network platform (Facebook, Twitter, Flickr). ● A tremendous amount of data in different forms including text, image, and videos makes sentiment analysis a very challenging task. ● In this chapter, we will discuss some of the latest works on topics of sentiment analysis based on visual content and textual content.

Multimedia data minig and analytics sentiment analysis using social multimedia from Kan-Han (John) Lu

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0 Android IPC: Binder /slideshow/android-ipc-binder/55917328 androidipc-151208032955-lva1-app6892
Introduce the Android IPC - Binder]]>
Introduce the Android IPC - Binder]]> Tue, 08 Dec 2015 03:29:55 GMT /slideshow/android-ipc-binder/55917328 lekaha@slideshare.net(lekaha) Android IPC: Binder lekaha Introduce the Android IPC - Binder <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/androidipc-151208032955-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduce the Android IPC - Binder

Android IPC: Binder from Kan-Han (John) Lu

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0 ARM: Trusted Zone on Android /slideshow/arm-trusted-zone-on-android/55917096 trustedzone-151208031645-lva1-app6892
Introduce TEE of ARM's TrustedZone on Android]]>
Introduce TEE of ARM's TrustedZone on Android]]> Tue, 08 Dec 2015 03:16:45 GMT /slideshow/arm-trusted-zone-on-android/55917096 lekaha@slideshare.net(lekaha) ARM: Trusted Zone on Android lekaha Introduce TEE of ARM's TrustedZone on Android <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/trustedzone-151208031645-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduce TEE of ARM's TrustedZone on Android

ARM: Trusted Zone on Android from Kan-Han (John) Lu

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0 Android Training - Card Style /slideshow/android-training-card-style/55917016 cardstyletraining-151208031312-lva1-app6891
The slide for a training course of Android.]]>
The slide for a training course of Android.]]> Tue, 08 Dec 2015 03:13:11 GMT /slideshow/android-training-card-style/55917016 lekaha@slideshare.net(lekaha) Android Training - Card Style lekaha The slide for a training course of Android. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cardstyletraining-151208031312-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> The slide for a training course of Android.

Android Training - Card Style from Kan-Han (John) Lu

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0 Android Training - View Pager /slideshow/android-training-view-pager/55916560 viewpagertraining-151208025003-lva1-app6892
The slide for a training course of Android.]]>
The slide for a training course of Android.]]> Tue, 08 Dec 2015 02:50:03 GMT /slideshow/android-training-view-pager/55916560 lekaha@slideshare.net(lekaha) Android Training - View Pager lekaha The slide for a training course of Android. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/viewpagertraining-151208025003-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> The slide for a training course of Android.

Android Training - View Pager from Kan-Han (John) Lu

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0 Android Training - Sliding Menu /slideshow/android-training-sliding-menu/55916554 slidingmenutraining-151208024951-lva1-app6892
The slide for a training course of Android.]]>
The slide for a training course of Android.]]> Tue, 08 Dec 2015 02:49:51 GMT /slideshow/android-training-sliding-menu/55916554 lekaha@slideshare.net(lekaha) Android Training - Sliding Menu lekaha The slide for a training course of Android. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidingmenutraining-151208024951-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> The slide for a training course of Android.

Android Training - Sliding Menu from Kan-Han (John) Lu

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0 Android Training - Pull to Refresh /slideshow/android-training-pull-to-refresh/55916553 pulltorefreshtraining-151208024944-lva1-app6891
The slide for a training course of Android.]]>
The slide for a training course of Android.]]> Tue, 08 Dec 2015 02:49:44 GMT /slideshow/android-training-pull-to-refresh/55916553 lekaha@slideshare.net(lekaha) Android Training - Pull to Refresh lekaha The slide for a training course of Android. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pulltorefreshtraining-151208024944-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> The slide for a training course of Android.

Android Training - Pull to Refresh from Kan-Han (John) Lu

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0 Java: Exception Handling /slideshow/java-exception-handling-55916548/55916548 exceptionhandling-151208024935-lva1-app6892
What and how exception handling]]>
What and how exception handling]]> Tue, 08 Dec 2015 02:49:35 GMT /slideshow/java-exception-handling-55916548/55916548 lekaha@slideshare.net(lekaha) Java: Exception Handling lekaha What and how exception handling <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/exceptionhandling-151208024935-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> What and how exception handling

Java: Exception Handling from Kan-Han (John) Lu

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0 Dynamic Proxy by Java /slideshow/dynamic-proxy-by-java/55916546 dynamicproxy-151208024931-lva1-app6891
Dynamic Proxy is a design pattern working with Java]]>
Dynamic Proxy is a design pattern working with Java]]> Tue, 08 Dec 2015 02:49:31 GMT /slideshow/dynamic-proxy-by-java/55916546 lekaha@slideshare.net(lekaha) Dynamic Proxy by Java lekaha Dynamic Proxy is a design pattern working with Java <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dynamicproxy-151208024931-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Dynamic Proxy is a design pattern working with Java

Dynamic Proxy by Java from Kan-Han (John) Lu

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0 Code analyzer: FindBugs and PMD /slideshow/code-analyzer-findbugs-and-pmd/55916536 codeanalyzer-findbugsandpmd-151208024835-lva1-app6892
Introduce two tools of Code analyzer]]>
Introduce two tools of Code analyzer]]> Tue, 08 Dec 2015 02:48:35 GMT /slideshow/code-analyzer-findbugs-and-pmd/55916536 lekaha@slideshare.net(lekaha) Code analyzer: FindBugs and PMD lekaha Introduce two tools of Code analyzer <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/codeanalyzer-findbugsandpmd-151208024835-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduce two tools of Code analyzer

Code analyzer: FindBugs and PMD from Kan-Han (John) Lu

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0 Android UI System /slideshow/android-ui-system/55916530 androiduisystem-151208024825-lva1-app6892
Introduce Android UI System in Framework layer]]>
Introduce Android UI System in Framework layer]]> Tue, 08 Dec 2015 02:48:25 GMT /slideshow/android-ui-system/55916530 lekaha@slideshare.net(lekaha) Android UI System lekaha Introduce Android UI System in Framework layer <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/androiduisystem-151208024825-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduce Android UI System in Framework layer

Android UI System from Kan-Han (John) Lu

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0 Android Fragment /slideshow/android-fragment-55916527/55916527 androidfragment-151208024818-lva1-app6892
Introduce Android Fragment]]>
Introduce Android Fragment]]> Tue, 08 Dec 2015 02:48:18 GMT /slideshow/android-fragment-55916527/55916527 lekaha@slideshare.net(lekaha) Android Fragment lekaha Introduce Android Fragment <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/androidfragment-151208024818-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduce Android Fragment

Android Fragment from Kan-Han (John) Lu

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0 Android Training - Content Sharing /slideshow/android-training-content-sharing/55916465 trainingcontentsharing-151208024533-lva1-app6891
The slide for a training course of Android.]]>
The slide for a training course of Android.]]> Tue, 08 Dec 2015 02:45:33 GMT /slideshow/android-training-content-sharing/55916465 lekaha@slideshare.net(lekaha) Android Training - Content Sharing lekaha The slide for a training course of Android. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/trainingcontentsharing-151208024533-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> The slide for a training course of Android.

Android Training - Content Sharing from Kan-Han (John) Lu

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0 https://cdn.slidesharecdn.com/profile-photo-lekaha-48x48.jpg?cb=1536658838 I am a full-stack engineer. Currently, I am working in a startup-scaled company in Japan. I am ambitious to change what I want to see in the world. I always give the maximum contribution in my work and study hard in my personal interests, i.e., Machine learning, Data Mining, and Design Pattern. I deeply love the things that can help people. Motto: Play hard and work smart! http://lekaha.blogspot.com/ https://cdn.slidesharecdn.com/ss_thumbnails/daggerforandroid-180208072933-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/dagger-for-android/87483801 Dagger for android https://cdn.slidesharecdn.com/ss_thumbnails/androiddevelopguideline-170613151323-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/android-develop-guideline/76905602 Android develop guideline https://cdn.slidesharecdn.com/ss_thumbnails/workingprocessandgitbranchstrategy1-170613150927-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/working-process-and-git-branch-strategy/76905447 Working process and gi...