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Malaysia R User Group Meetup March 2019 at Microsoft Malaysia
Build an efficient Machine Learning model with LightGBM
Build an efficient Machine Learning model with LightGBM
Poo Kuan Hoong
TensorFlow & Deep Learning Malaysia Meetup (Feb 2019)
Tensor flow 2.0 what's new
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Data Science Talk at Faculty of Computer Science and Information Technology, Universiti Malaya, 20th March 2018
The future outlook and the path to be Data Scientist
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Applied Deep Learning Preview at MaGic Malaysia
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Classifying Cats and Dogs - Google i/O 2017 Extended Kuala Lumpur
Explore and Have Fun with TensorFlow: Transfer Learning
Explore and Have Fun with TensorFlow: Transfer Learning
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Malaysia R User Group Meetup at Microsoft Malaysia, 13th July 2017. Facebook Page https://www.facebook.com/rusergroupmalaysia/ Video of the talk can be viewed here https://www.youtube.com/watch?v=lN057ua0dKU
Deep Learning with R
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Poo Kuan Hoong
TensorFlow and Deep Learning Malaysia Meetup 6th July 2017 https://www.facebook.com/groups/TensorFlowMY and recorded video of the talk can be viewed here https://www.youtube.com/watch?v=EdMz3fwRFBc
Explore and have fun with TensorFlow: An introductory to TensorFlow
Explore and have fun with TensorFlow: An introductory to TensorFlow
Poo Kuan Hoong
Knowledge Sharing Session with Faculty of Applied Sciences and Computing, Tunku Abdul Rahman University College, 21st June 2017
The path to be a Data Scientist
The path to be a Data Scientist
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Malaysia R User Group Meetup March 2019 at Microsoft Malaysia
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Poo Kuan Hoong
TensorFlow & Deep Learning Malaysia Meetup (Feb 2019)
Tensor flow 2.0 what's new
Tensor flow 2.0 what's new
Poo Kuan Hoong
Data Science Talk at Faculty of Computer Science and Information Technology, Universiti Malaya, 20th March 2018
The future outlook and the path to be Data Scientist
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Poo Kuan Hoong
Applied Deep Learning Preview at MaGic Malaysia
TensorFlow and Keras: An Overview
TensorFlow and Keras: An Overview
Poo Kuan Hoong
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Explore and Have Fun with TensorFlow: Transfer Learning
Explore and Have Fun with TensorFlow: Transfer Learning
Poo Kuan Hoong
Malaysia R User Group Meetup at Microsoft Malaysia, 13th July 2017. Facebook Page https://www.facebook.com/rusergroupmalaysia/ Video of the talk can be viewed here https://www.youtube.com/watch?v=lN057ua0dKU
Deep Learning with R
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Poo Kuan Hoong
TensorFlow and Deep Learning Malaysia Meetup 6th July 2017 https://www.facebook.com/groups/TensorFlowMY and recorded video of the talk can be viewed here https://www.youtube.com/watch?v=EdMz3fwRFBc
Explore and have fun with TensorFlow: An introductory to TensorFlow
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Poo Kuan Hoong
Knowledge Sharing Session with Faculty of Applied Sciences and Computing, Tunku Abdul Rahman University College, 21st June 2017
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Poo Kuan Hoong
Microsoft APAC Machine Learning and Data Science Summit at Seoul Korea
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Poo Kuan Hoong
Microsoft APAC Machine Learning & Data Science Community Bootcamp at Microsoft Seoul, Korea
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Microsoft APAC Machine Learning & Data Science Community Bootcamp
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Malaysia R User Group Meetup, 16th February 2017
Customer Churn Analytics using Microsoft R Open
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Poo Kuan Hoong
Deep Learning Malaysia Meetup, 15th February 2017. MNIST Handwritten Digits Recognition with MXNet R Package
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Data Science with Python event at UTM, Menara Razak, Kuala Lumpur
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MDEC Data Matters Series Talk at Impiana KLCC Hotel, 21st September 2016
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MDEC Data Matters Series: machine learning and Deep Learning, A Primer
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Big Data Malaysia talk, 19th September 2016 at Melur & Thyme KLCC
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Big Data Malaysia - A Primer on Deep Learning
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R User Group Malaysia Meet Up - Handwritten Recognition using Deep Learning with R Source code available at: https://github.com/kuanhoong/myRUG_DeepLearning
Handwritten Recognition using Deep Learning with R
Handwritten Recognition using Deep Learning with R
Poo Kuan Hoong
DevEx - Tech Talk https://www.facebook.com/events/891637610968252/
An Introduction to Deep Learning
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Poo Kuan Hoong
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Poo Kuan Hoong
This project focuses on developing a mobile application that transmits real-time traffic state to motorcyclists. The traffic data is collected from Twitter. The data collected is subjected to various processes like Named Entity Recognition, Sentiment Analysis and Statistical Analysis and the derived traffic state will be transmitted to the user’s mobile application. A Bluetooth enabled helmet of a motorcyclist will then playback the traffic state to the user according to their location.
Context Aware Road Traffic Speech Information System from Social Media
Context Aware Road Traffic Speech Information System from Social Media
Poo Kuan Hoong
This project focuses on developing a mobile application that integrates Google Cardboard and Myo Armband. The mobile application developed is an educational application that teaches users to write Japanese characters by getting users to trace the characters display on the phone screen. The users will air draw the letters while the Myo Armband will capture the gestures, send the data to the smartphones and display the drawn character on the screen.
Virtual Interaction Using Myo And Google Cardboard (slides)
Virtual Interaction Using Myo And Google Cardboard (slides)
Poo Kuan Hoong
Social Networks such as Facebook, Twitter, Google+ and LinkedIn have millions of users. These networks are constantly evolving and it is a good source of information, both explicitly and implicitly. The analysis of Social Network mainly focuses on the aspect of social networking with an emphasis on mapping relationships, patterns of interaction between user and content information. One of the common research topics focuses on the centrality measures where useful information of the connected people in the social network is represented in a graph. In this paper, we employed two link-based ranking algorithms to analyze the ranking of the users: HITS (Hyperlink- Induced Topic Search) and PageRank. We constructed Twitter user retweet-relationship graph using 21 days worth of data. Lastly, we compared the ranking sequence of the users in addition to their followers count against the average and also whether they are verified Twitter accounts. From the results obtained, both HITS and PageRank showed a similar trend, and more importantly highlighted the importance of the direction of the edges in this work.
A Comparative Study of HITS vs PageRank Algorithms for Twitter Users Analysis
A Comparative Study of HITS vs PageRank Algorithms for Twitter Users Analysis
Poo Kuan Hoong
There have been numerous interests in the area of detecting availability of car park bay using image processing techniques instead of utilizing expensive sensors. An area that has been neglected in doing so is the initial calibration of the image capturing device on the need to determine the car park structures. This paper proposes a technique that addresses this issue, using the limited processing capabilities of embedded systems. The results are promising, where in its current form, is semi-automated calibration for the car park structure detection and further enhancements can be made, to make it completely automated
Towards Auto-Extracting Car Park Structures: Image Processing Approach on Low...
Towards Auto-Extracting Car Park Structures: Image Processing Approach on Low...
Poo Kuan Hoong
With the rising popularity of social media such as Facebook, Twitter, Instagram and many more, sentiment classification for social media has become a hot research topic. There were many research studies conducted on Twitter as it is one of the most widely used social media. Previous studies have approached the problem as a tweet-level classification task where each tweet is classified as positive, negative or neutral. However, getting an overall sentiment might not be useful to a business organizations which are using Twitter for monitoring consumer opinion of their products/services. Instead, it is more useful to determine specifically which tweets where users are happy or unhappy about. This paper proposes the discovery of Twitter user level interestingness based on relationships such as retweets, reply-mentions and pure-mentions using Google's PageRank algorithm. We conducted experiments and compared the results with hard-marked results by seven annotators.
Discovery of Twitter User Interestingness Based on Retweets, Reply Mentions a...
Discovery of Twitter User Interestingness Based on Retweets, Reply Mentions a...
Poo Kuan Hoong
Real-time human detection and tracking from video surveillance footages is one of the most active research areas in computer vision and pattern recognition. This is due to the widespread application from being able to do it well. One such application is the counting of people, or density estimation, where the two key components are human detection and tracking. Traditional methods such as the usage of sensors are not suitable as they are not easily integrated with current video surveillance systems. As video surveillance systems are currently prevalent in most places, using vision based people counting techniques will be the logical approach. In this paper, we compared the two commonly used techniques which are Cascade Classifier and Histograms of Gradients (HOG) for human detection. We evaluated and compared these two techniques with three different video datasets with three different setting characteristics. From our experiment results, both Cascade Classifier and HOG techniques can be used for people counting to achieve moderate accuracy results.
A Comparison of People Counting Techniques viaVideo Scene Analysis
A Comparison of People Counting Techniques viaVideo Scene Analysis
Poo Kuan Hoong
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Building Real-Time Pipelines With FLaNK Timothy Spann, Principal Developer Advocate, Streaming - Cloudera Future of Data meetup, startup grind, AI Camp The combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines is extremely powerful, as demonstrated by this case study using the FLaNK-MTA project. The project leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making. Apache NiFi Apache Kafka Apache Flink Apache Iceberg LLM Generative AI Slack Postgresql
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK
Timothy Spann
Digital advertising, or paid media, encompasses the strategic deployment of online advertisements to reach target audiences efficiently and effectively. This includes any digital platform that supports advertising to deliver unique messages for any objective. Understanding the mechanics of digital advertising platforms, along with insights into audience behaviors and preferences, allows marketers to optimize their ad spend and achieve significant engagement and conversion rates. This lecture is for Advanced Digital & Social Media Strategy (MGMTX 466.05) at UCLA Extension.
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
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Aspirational Block Programme Block - Syaldey District - Almora Month of April Progress report
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Microsoft APAC Machine Learning and Data Science Summit at Seoul Korea
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This project focuses on developing a mobile application that transmits real-time traffic state to motorcyclists. The traffic data is collected from Twitter. The data collected is subjected to various processes like Named Entity Recognition, Sentiment Analysis and Statistical Analysis and the derived traffic state will be transmitted to the user’s mobile application. A Bluetooth enabled helmet of a motorcyclist will then playback the traffic state to the user according to their location.
Context Aware Road Traffic Speech Information System from Social Media
Context Aware Road Traffic Speech Information System from Social Media
Poo Kuan Hoong
This project focuses on developing a mobile application that integrates Google Cardboard and Myo Armband. The mobile application developed is an educational application that teaches users to write Japanese characters by getting users to trace the characters display on the phone screen. The users will air draw the letters while the Myo Armband will capture the gestures, send the data to the smartphones and display the drawn character on the screen.
Virtual Interaction Using Myo And Google Cardboard (slides)
Virtual Interaction Using Myo And Google Cardboard (slides)
Poo Kuan Hoong
Social Networks such as Facebook, Twitter, Google+ and LinkedIn have millions of users. These networks are constantly evolving and it is a good source of information, both explicitly and implicitly. The analysis of Social Network mainly focuses on the aspect of social networking with an emphasis on mapping relationships, patterns of interaction between user and content information. One of the common research topics focuses on the centrality measures where useful information of the connected people in the social network is represented in a graph. In this paper, we employed two link-based ranking algorithms to analyze the ranking of the users: HITS (Hyperlink- Induced Topic Search) and PageRank. We constructed Twitter user retweet-relationship graph using 21 days worth of data. Lastly, we compared the ranking sequence of the users in addition to their followers count against the average and also whether they are verified Twitter accounts. From the results obtained, both HITS and PageRank showed a similar trend, and more importantly highlighted the importance of the direction of the edges in this work.
A Comparative Study of HITS vs PageRank Algorithms for Twitter Users Analysis
A Comparative Study of HITS vs PageRank Algorithms for Twitter Users Analysis
Poo Kuan Hoong
There have been numerous interests in the area of detecting availability of car park bay using image processing techniques instead of utilizing expensive sensors. An area that has been neglected in doing so is the initial calibration of the image capturing device on the need to determine the car park structures. This paper proposes a technique that addresses this issue, using the limited processing capabilities of embedded systems. The results are promising, where in its current form, is semi-automated calibration for the car park structure detection and further enhancements can be made, to make it completely automated
Towards Auto-Extracting Car Park Structures: Image Processing Approach on Low...
Towards Auto-Extracting Car Park Structures: Image Processing Approach on Low...
Poo Kuan Hoong
With the rising popularity of social media such as Facebook, Twitter, Instagram and many more, sentiment classification for social media has become a hot research topic. There were many research studies conducted on Twitter as it is one of the most widely used social media. Previous studies have approached the problem as a tweet-level classification task where each tweet is classified as positive, negative or neutral. However, getting an overall sentiment might not be useful to a business organizations which are using Twitter for monitoring consumer opinion of their products/services. Instead, it is more useful to determine specifically which tweets where users are happy or unhappy about. This paper proposes the discovery of Twitter user level interestingness based on relationships such as retweets, reply-mentions and pure-mentions using Google's PageRank algorithm. We conducted experiments and compared the results with hard-marked results by seven annotators.
Discovery of Twitter User Interestingness Based on Retweets, Reply Mentions a...
Discovery of Twitter User Interestingness Based on Retweets, Reply Mentions a...
Poo Kuan Hoong
Real-time human detection and tracking from video surveillance footages is one of the most active research areas in computer vision and pattern recognition. This is due to the widespread application from being able to do it well. One such application is the counting of people, or density estimation, where the two key components are human detection and tracking. Traditional methods such as the usage of sensors are not suitable as they are not easily integrated with current video surveillance systems. As video surveillance systems are currently prevalent in most places, using vision based people counting techniques will be the logical approach. In this paper, we compared the two commonly used techniques which are Cascade Classifier and Histograms of Gradients (HOG) for human detection. We evaluated and compared these two techniques with three different video datasets with three different setting characteristics. From our experiment results, both Cascade Classifier and HOG techniques can be used for people counting to achieve moderate accuracy results.
A Comparison of People Counting Techniques viaVideo Scene Analysis
A Comparison of People Counting Techniques viaVideo Scene Analysis
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Building Real-Time Pipelines With FLaNK Timothy Spann, Principal Developer Advocate, Streaming - Cloudera Future of Data meetup, startup grind, AI Camp The combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines is extremely powerful, as demonstrated by this case study using the FLaNK-MTA project. The project leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making. Apache NiFi Apache Kafka Apache Flink Apache Iceberg LLM Generative AI Slack Postgresql
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK
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Timothy Spann
Digital advertising, or paid media, encompasses the strategic deployment of online advertisements to reach target audiences efficiently and effectively. This includes any digital platform that supports advertising to deliver unique messages for any objective. Understanding the mechanics of digital advertising platforms, along with insights into audience behaviors and preferences, allows marketers to optimize their ad spend and achieve significant engagement and conversion rates. This lecture is for Advanced Digital & Social Media Strategy (MGMTX 466.05) at UCLA Extension.
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