yanliang1102 Goto Github PK
Name: YanLiang
Type: User
Company: Amazon
Location: Seattle WA
Blog: https://www.linkedin.com/hp/?dnr=O_YnLI8JZeIdkP1GO_r_LRTAx747kPuZNfmr
Name: YanLiang
Type: User
Company: Amazon
Location: Seattle WA
Blog: https://www.linkedin.com/hp/?dnr=O_YnLI8JZeIdkP1GO_r_LRTAx747kPuZNfmr
Official content for the Fall 2014 Harvard CS109 Data Science course
Data directory for the CS109 Data Science course
Source code from the Build 2014 presentation by David Catuhe and Jon Galloway
Building a Data-Driven App with AngularJS with Ray Villalobos
Official content for Harvard CS109
Neural Networks for Machine Learning (University of Toronto)
Programming assignments from all courses in the Coursera Natural Language Processing Specialization offered by deeplearning.ai.
CS224n: Natural Language Processing with Deep Learning Assignments Winter, 2017
EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit
A list of NLP resources focused on event extraction task
免费的计算机编程类中文书籍,欢迎投稿
Source code for EMNLP-IJCNLP 2019 paper "HMEAE: Hierarchical Modular Event Argument Extraction".
《统计学习方法》的代码实现
Classify handwritten digits using machine learning techniques Yan Liang, Yunzhi Wang and Delong Zhao Project scope For our machine learning project, we propose to build several machine learning classifiers that recognize handwritten digits. Handwritten digit recognition is a classic problem in machine learning studies for many years. We plan to do several experiments using different machine learning algorithms and compare the pattern recognition performance. We hope to create a classifier that has same or better categorization accuracy than record performance from previous studies. Yan will focus on neural network, Delong will focus on the random forests methods, and Yunzhi will focus on SVMs and KNNs. We will also develop a final novel classifier that combines the best models from our different experiments. We hypothesize that the final classifier will archive a categorization accuracy of 0.99. This indicates that the classifier correctly classified all the handwritten digits but 1% of the images. The goal of handwritten digit recognition is to determine what digit is from an image of a single handwritten digit. It can be used to test pattern recognition theories and machine learning algorithms. Preprocessed standard handwritten digit image database has been developed to compare different digit recognizers. In our semester project, we will use modified National Institute of Standards and Technology (MNIST) handwritten digit images dataset from kaggle digit recognizer project. The Kaggle MNIST dataset is freely available and collected 28,000 training images and 42,000 test images. Each image is a preprocessed single black and white digit image with 28 x 28 pixels. Each pixel is an integer value range from 0 to 255 which represent the brightness of the pixel, the higher value meaning darker. Each image also has a label which is the correct digit for the handwritten image. For each input handwritten image, our model will output which digit we predict and evaluate with the correct label. We will use 28,000 training images to train our machine learning model and use 42,000 test images to test the performance. Then we will calculate the percentage of the test images that are correctly classified and compare the performance of different machine learning algorithms.
Source code and dataset for EMNLP 2020 paper "MAVEN: A Massive General Domain Event Detection Dataset".
machine learning code in spark
N3 - A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format
OU-ISE5103-FinalProject-StockPrediction
parallel programming code
A declarative, efficient, and flexible JavaScript library for building user interfaces.
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TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
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JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.