A Git tutorial with example and demo.
YouTube Videos
Day 1: https://www.youtube.com/watch?v=XBreuLi79bk
Day 2: https://www.youtube.com/watch?v=5e6i3r5Vz34
A Git tutorial with example and demo.
YouTube Videos
Day 1: https://www.youtube.com/watch?v=XBreuLi79bk
Day 2: https://www.youtube.com/watch?v=5e6i3r5Vz34
1. A brief introduction of Git (SVN, CVCS, DVCS etc.)
2. Git/TortoiseGit/msysgit installations
3. A complete guide of Git operations
4. The Branches and HEAD
5. Remote and local repository
6. Rebase and submodules etc.
7. Some Skills and Experience
This document provides an introduction and outline for a Python fundamentals course focused on basic Python programming. The outline covers topics like introduction, data types, operators, flow control, functions, classes, exceptions, and file input/output. Code examples are provided for common concepts like Hello World, variables, conditionals, loops, functions, and classes. The goal is to teach Python basics to beginners.
Scientific Computing with Python - NumPy | WeiYuanWei-Yuan Chang
This document provides an overview of NumPy, the fundamental package for scientific computing in Python. It discusses NumPy's powerful N-dimensional array object and sophisticated broadcasting functions. The document outlines topics including Ndarray, creating and manipulating arrays, array properties, basic operations, matrices, and advanced usages like boolean indexing and masking. NumPy allows efficient storage and manipulation of multi-dimensional data, and integration with languages like C/C++ and Fortran.
The document provides an introduction to basic web development technologies including HTML, CSS, and JavaScript. It outlines the main elements of each technology - HTML is used for page structure and content, CSS controls styling and layout, and JavaScript adds interactive behaviors. Code examples are shown for creating a basic HTML page structure and adding CSS styles and JavaScript alerts. The document aims to give a broad overview of the core languages used for frontend web development.
This document provides an outline for a lesson on data visualization using D3.js. The outline includes sections on introducing D3.js, setting up the environment, examples of selections and appending data, using SVG, data binding, styling with attributes, creating bar charts and plot charts, transitions, scales, axes, and charts using C3.js. It also references additional resources on data visualization and examples of charts created with different tools.
This document is a JavaScript beginner tutorial that outlines topics including:
- The history and evolution of JavaScript from 1995 to present
- How JavaScript is used with HTML and CSS
- Common JavaScript types like numbers, strings, arrays, and objects
- Flow control with if/else statements and loops
- Functions and function declarations
- Advanced JavaScript concepts
The tutorial provides examples and explanations of core JavaScript concepts to help beginners learn the fundamentals of the language. It outlines the structure and flow of the content that will be covered.
This document provides an overview of Python fundamentals including basic concepts like data types, operators, flow control, functions and classes. It begins with an introduction to Python versions and environments. The outline covers topics like Hello World, common types and operators for numeric, string and container data types. It also discusses flow control structures like if/else, while loops and for loops. Finally, it briefly mentions functions, classes, exceptions and file I/O.
Analysis and Classification of Respiratory Health Risks with Respect to Air P...Wei-Yuan Chang
This document analyzes the relationship between air pollution levels and respiratory health risks in Dhaka, Bangladesh. It uses k-means clustering to group air quality and disease admission data. A decision tree is generated to predict disease admission levels based on air pollution data and other factors. The models for COPD and ILD achieved over 50% accuracy but the model for bronchitis carcinoma was not applicable due to low accuracy, indicating other diagnosis factors are also important. The study aims to classify respiratory patients and air pollutants in Dhaka according to admission rates and seasons using data mining techniques.
Forecasting Fine Grained Air Quality Based on Big DataWei-Yuan Chang
1) The document proposes a system to forecast fine-grained air quality up to 48 hours in the future using a hybrid predictive model.
2) The model combines temporal prediction based on a station's own historical data with spatial prediction based on data from nearby stations.
3) The model also considers current and forecasted meteorological data as well as inflection points in the historical air quality data.
On the Coverage of Science in the Media a Big Data Study on the Impact of th...Wei-Yuan Chang
This document summarizes a study that analyzed over 5 million online science news articles to explore how media coverage and attitudes toward nuclear power changed after the 2011 Fukushima disaster in Japan. Key findings include that attention to and sentiment around nuclear power significantly changed following the event, with attention increasing and sentiment becoming more negative. The study used techniques like extracting references to topics, generating time series, and mining associations to analyze shifts in media discourse regarding nuclear power before and after the Fukushima disaster.
On the Ground Validation of Online Diagnosis with Twitter and Medical RecordsWei-Yuan Chang
This document summarizes a research paper that developed a novel system for social-media based disease detection at the individual level. It collected Twitter data and medical records from 104 students, including 35 diagnosed with influenza. It analyzed the Twitter data using several methods: automated text classification with expert and machine-selected keywords, anomaly detection of tweeting rates, and network classification of friends' tweets. A meta-classifier was used to aggregate the separate classifiers and increase classification accuracy. The results showed it was possible to accurately diagnose an individual's disease from their social media data combined with medical records.
Effective Event Identification in Social MediaWei-Yuan Chang
This document summarizes a method for effective event identification in social media. It describes using clustering to group similar social media documents into events, both for known and unknown events. Key aspects of the method include using multiple document features like text, hashtags and timestamps. An ensemble algorithm learns weights for these features. Techniques like considering bursty vocabulary terms and time decay help improve clustering effectiveness over time. An evaluation on real-world event data from Flickr found the method using bursty vocabulary and time decay obtained the highest quality results.
Fine Grained Location Extraction from Tweets with Temporal AwarenessWei-Yuan Chang
This document summarizes a research paper on extracting fine-grained location information from tweets with temporal awareness. It presents the PETAR method which involves building a POI inventory from Foursquare check-in data, analyzing tweet data to observe location and temporal patterns, and developing a time-aware POI tagger using lexical, grammatical, and geographical features to simultaneously disambiguate POI mentions and resolve temporal awareness. An experiment compares PETAR to other methods and finds PETAR performs best by leveraging both lexical and grammatical features. The conclusion is that PETAR facilitates location-based services by extracting fine-grained locations and temporal awareness from tweets.
Practical Lessons from Predicting Clicks on Ads at FacebookWei-Yuan Chang
The document discusses techniques for predicting clicks on ads at Facebook. It describes using a hybrid model with boosted decision trees trained daily for feature transforms, and a logistic regression linear classifier trained in real-time using an online data joiner. The online joiner generates training data by performing a distributed stream-to-stream join of ad impressions and clicks. Experiments showed historical features and adjusting the number of boosting trees improved performance. The techniques provided a promising architecture for click prediction at scale.
How many folders do you really need ? Classifying email into a handful of cat...Wei-Yuan Chang
This document summarizes a research paper that presents an approach for automatically classifying emails into latent categories without requiring users to define folders. It uses LDA to discover latent categories from message content and headers. A two-stage classifier is created using message-level and sender-level features to first classify known senders, then apply heavier models to a small percentage of remaining messages. The approach was tested on a large Yahoo mail dataset consisting of over 500 billion messages over 6 months. Results showed the method could accurately discriminate between human and machine-generated emails while organizing emails into meaningful categories.
Discovering human places of interest from multimodal mobile phone dataWei-Yuan Chang
This document summarizes a framework for discovering human places of interest from multimodal mobile phone data. It presents time-based and grid-based clustering methods to analyze location point data from GPS, WiFi maps, and stationary sensors to identify stay points and stay regions representing places visited by users. An experiment found the framework could obtain location data for 63% of a day and identified places with improved accuracy over alternative approaches.