Processing Tutorial - Create your interactive program in a click~CAVEDU Education
Using Processing 2 to create various kinds of interactive programs and games. Further you can combine Processing with Arduino and Android phone.
More interesting: http://www.cavedu.com
A step-by-step tutorial to start a deep learning startup. Deep learning is a specialty of artificial intelligence, based on neural networks. I explain how I launched my face recognition startup: Mindolia.com
This is the slide that Terry. T. Um gave a presentation at Kookmin University in 22 June, 2014. Feel free to share it and please let me know if there is some misconception or something.
(http://t-robotics.blogspot.com)
(http://terryum.io)
Indoor Point Cloud Processing - Deep learning for semantic segmentation of in...CubiCasa
This document discusses using deep learning techniques for semantic segmentation of indoor point clouds. It provides an overview of initial ideas for using deep learning models trained on 3D CAD models to classify and label points in an indoor point cloud. It also discusses pre-processing the point cloud through techniques like denoising, upsampling, and finding planar surfaces to simplify the input before semantic segmentation. The order of semantic segmentation and 3D reconstruction is noted as something that could potentially be swapped.
Scalable Data Science and Deep Learning with H2O
In this session, we introduce the H2O data science platform. We will explain its scalable in-memory architecture and design principles and focus on the implementation of distributed deep learning in H2O. Advanced features such as adaptive learning rates, various forms of regularization, automatic data transformations, checkpointing, grid-search, cross-validation and auto-tuning turn multi-layer neural networks of the past into powerful, easy-to-use predictive analytics tools accessible to everyone. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases.
By the end of the hands-on-session, attendees will have learned to perform end-to-end data science workflows with H2O using both the easy-to-use web interface and the flexible R interface. We will cover data ingest, basic feature engineering, feature selection, hyperparameter optimization with N-fold cross-validation, multi-model scoring and taking models into production. We will train supervised and unsupervised methods on realistic datasets. With best-of-breed machine learning algorithms such as elastic net, random forest, gradient boosting and deep learning, you will be able to create your own smart applications.
A local installation of RStudio is recommended for this session.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document provides an introduction to exploring and visualizing data using the R programming language. It discusses the history and development of R, introduces key R packages like tidyverse and ggplot2 for data analysis and visualization, and provides examples of reading data, examining data structures, and creating basic plots and histograms. It also demonstrates more advanced ggplot2 concepts like faceting, mapping variables to aesthetics, using different geoms, and combining multiple geoms in a single plot.
This document discusses applying data mining techniques to analyze active users on Reddit. It defines active users as those who posted or commented in at least 5 subreddits and have at least 5 posts/comments in each subreddit. The preprocessing steps extract over 25,000 active users and their posts from the raw Reddit data. K-means clustering is then used to cluster the active users into 10 groups based on their activities to gain insights into different types of active users on Reddit.
在這資料科學逐漸成為顯學的年代,無論面對的是資料的幾個 V,其中最重要的永遠都是 Value (價值) 這個 V,而資料探勘正是一種透過系統化的方式釐清資料的脈絡、找出其中有價值的特徵與相關性的技術。這門六小時的課程,將從最實務的角度切入,與大家分享如何將現實中極待解決的問題,轉換成可以利用資料探勘技術處理的問題,並且運用 R 語言中各種強大的工具,進行關聯性分析、迴歸分析以及叢聚分析,以達成將資料中隱藏的資訊挖掘出來的最終目標。
H2O Distributed Deep Learning by Arno Candel 071614Sri Ambati
Deep Learning R Vignette Documentation: https://github.com/0xdata/h2o/tree/master/docs/deeplearning/
Deep Learning has been dominating recent machine learning competitions with better predictions. Unlike the neural networks of the past, modern Deep Learning methods have cracked the code for training stability and generalization. Deep Learning is not only the leader in image and speech recognition tasks, but is also emerging as the algorithm of choice in traditional business analytics.
This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of enterprise-scale problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization and optimization for class imbalance. World record performance on the classic MNIST dataset, best-in-class accuracy for eBay text classification and others showcase the power of this game changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
About the Speaker: Arno Candel
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world's largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes.
He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Processing Tutorial - Create your interactive program in a click~CAVEDU Education
Using Processing 2 to create various kinds of interactive programs and games. Further you can combine Processing with Arduino and Android phone.
More interesting: http://www.cavedu.com
A step-by-step tutorial to start a deep learning startup. Deep learning is a specialty of artificial intelligence, based on neural networks. I explain how I launched my face recognition startup: Mindolia.com
This is the slide that Terry. T. Um gave a presentation at Kookmin University in 22 June, 2014. Feel free to share it and please let me know if there is some misconception or something.
(http://t-robotics.blogspot.com)
(http://terryum.io)
Indoor Point Cloud Processing - Deep learning for semantic segmentation of in...CubiCasa
This document discusses using deep learning techniques for semantic segmentation of indoor point clouds. It provides an overview of initial ideas for using deep learning models trained on 3D CAD models to classify and label points in an indoor point cloud. It also discusses pre-processing the point cloud through techniques like denoising, upsampling, and finding planar surfaces to simplify the input before semantic segmentation. The order of semantic segmentation and 3D reconstruction is noted as something that could potentially be swapped.
Scalable Data Science and Deep Learning with H2O
In this session, we introduce the H2O data science platform. We will explain its scalable in-memory architecture and design principles and focus on the implementation of distributed deep learning in H2O. Advanced features such as adaptive learning rates, various forms of regularization, automatic data transformations, checkpointing, grid-search, cross-validation and auto-tuning turn multi-layer neural networks of the past into powerful, easy-to-use predictive analytics tools accessible to everyone. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases.
By the end of the hands-on-session, attendees will have learned to perform end-to-end data science workflows with H2O using both the easy-to-use web interface and the flexible R interface. We will cover data ingest, basic feature engineering, feature selection, hyperparameter optimization with N-fold cross-validation, multi-model scoring and taking models into production. We will train supervised and unsupervised methods on realistic datasets. With best-of-breed machine learning algorithms such as elastic net, random forest, gradient boosting and deep learning, you will be able to create your own smart applications.
A local installation of RStudio is recommended for this session.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document provides an introduction to exploring and visualizing data using the R programming language. It discusses the history and development of R, introduces key R packages like tidyverse and ggplot2 for data analysis and visualization, and provides examples of reading data, examining data structures, and creating basic plots and histograms. It also demonstrates more advanced ggplot2 concepts like faceting, mapping variables to aesthetics, using different geoms, and combining multiple geoms in a single plot.
This document discusses applying data mining techniques to analyze active users on Reddit. It defines active users as those who posted or commented in at least 5 subreddits and have at least 5 posts/comments in each subreddit. The preprocessing steps extract over 25,000 active users and their posts from the raw Reddit data. K-means clustering is then used to cluster the active users into 10 groups based on their activities to gain insights into different types of active users on Reddit.
在這資料科學逐漸成為顯學的年代,無論面對的是資料的幾個 V,其中最重要的永遠都是 Value (價值) 這個 V,而資料探勘正是一種透過系統化的方式釐清資料的脈絡、找出其中有價值的特徵與相關性的技術。這門六小時的課程,將從最實務的角度切入,與大家分享如何將現實中極待解決的問題,轉換成可以利用資料探勘技術處理的問題,並且運用 R 語言中各種強大的工具,進行關聯性分析、迴歸分析以及叢聚分析,以達成將資料中隱藏的資訊挖掘出來的最終目標。
H2O Distributed Deep Learning by Arno Candel 071614Sri Ambati
Deep Learning R Vignette Documentation: https://github.com/0xdata/h2o/tree/master/docs/deeplearning/
Deep Learning has been dominating recent machine learning competitions with better predictions. Unlike the neural networks of the past, modern Deep Learning methods have cracked the code for training stability and generalization. Deep Learning is not only the leader in image and speech recognition tasks, but is also emerging as the algorithm of choice in traditional business analytics.
This talk introduces Deep Learning and implementation concepts in the open-source H2O in-memory prediction engine. Designed for the solution of enterprise-scale problems on distributed compute clusters, it offers advanced features such as adaptive learning rate, dropout regularization and optimization for class imbalance. World record performance on the classic MNIST dataset, best-in-class accuracy for eBay text classification and others showcase the power of this game changing technology. A whole new ecosystem of Intelligent Applications is emerging with Deep Learning at its core.
About the Speaker: Arno Candel
Prior to joining 0xdata as Physicist & Hacker, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world's largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives. While at SLAC, he authored the first curvilinear finite-element simulation code for space-charge dominated relativistic free electrons and scaled it to thousands of compute nodes.
He also led a collaboration with CERN to model the electromagnetic performance of CLIC, a ginormous e+e- collider and potential successor of LHC. Arno has authored dozens of scientific papers and was a sought-after academic conference speaker. He holds a PhD and Masters summa cum laude in Physics from ETH Zurich.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
a slide share to convey this refreshing and well done research towards mobile marketing. -- only made it with a version to deliver the information, the data and the research owned entirely by the journal.
The opportunities for growth in China are immense for foreign companies — but so too are the risks and challenges. As your emerging or established brand enters new markets, G & G will be there to help you get access to the Chinese market, work out strategies, identify opportunities and make international business happen. We can:
Provide a one-stop service (from registration, legal counsel, facilities rental to human resources services) to foreign companies planning to extend their business operations into China, negotiating and obtaining the best terms for clients in terms of tax, office rental etc.
Develop a “market entry” strategy that includes land leasing, registration, corporate communications aimed at establishing brands, public affairs programs, and — if appropriate — a financial communications plan to support raising of capital.
G & G's network of experts positioned throughout the Greater China region has a proven record of promoting understanding, building brands, and managing reputations for the growing roster of ambitious foreign companies seeking opportunities in China.
Specialties
Landing in China, Integrated Marketing, Digital Marketing, Training, Advertising & Media buy, Branding, Events and TradeShows, Internal Communications, Marketing and Communications Strategy, Market Research, Public Relations, Traditional Marketing Tools Development