Pinned Repositories
AspNetCoreRateLimit
ASP.NET Core rate limiting middleware
awesome-github-repo
A curated list of awesome GitHub repositories. Inspired by awesome-python, which is inspired by awesome-php.
ccew-ios-fall-2015
CCEW Fall 2015 iOS Curriculum
Cryptography
CS5153 Network Security Projects
decisiontrees
Implementations of decision tree construction algorithms.
django
The Web framework for perfectionists with deadlines.
DJI_Super-Patcher
Liberate DJI drones. Height limit, NFZ limit, enable Galileo Satellites + more
flask
A microframework based on Werkzeug, Jinja2 and good intentions
Git-Cheat-Sheet
Git Cheat Sheet, the Chinese version by Gevin(flyhigher139)
machine-learning-classify-handwritten-digit
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.
zhaodelong's Repositories
zhaodelong/machine-learning-classify-handwritten-digit
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.
zhaodelong/AspNetCoreRateLimit
ASP.NET Core rate limiting middleware
zhaodelong/awesome-github-repo
A curated list of awesome GitHub repositories. Inspired by awesome-python, which is inspired by awesome-php.
zhaodelong/ccew-ios-fall-2015
CCEW Fall 2015 iOS Curriculum
zhaodelong/Cryptography
CS5153 Network Security Projects
zhaodelong/django
The Web framework for perfectionists with deadlines.
zhaodelong/DJI_Super-Patcher
Liberate DJI drones. Height limit, NFZ limit, enable Galileo Satellites + more
zhaodelong/flask
A microframework based on Werkzeug, Jinja2 and good intentions
zhaodelong/Git-Cheat-Sheet
Git Cheat Sheet, the Chinese version by Gevin(flyhigher139)
zhaodelong/grafana
The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
zhaodelong/guava
Google Core Libraries for Java 6+
zhaodelong/heartbleed-masstest
Multi-threaded tool for scanning many hosts for CVE-2014-0160.
zhaodelong/Kaggle-Digit
Random Forests, AdaBoost , ExtraTrees Algorithms applied
zhaodelong/leetcode-ext
:monkey: Chrome extension for leetcode
zhaodelong/lintcode
Lintcode solution in Java.
zhaodelong/ml_class
Machine Learning class notes
zhaodelong/MusicStore
zhaodelong/mygithubpage
zhaodelong/ok-coders-spring-2015
zhaodelong/Pair-Test-with-Mo-Di
zhaodelong/Pocsuite
Pocsuite 是知道创宇安全研究团队打造的一款基于漏洞与 PoC 的远程漏洞验证框架，Pocsuite is A remote vulnerability test framework developed by Knownsec Security Team.
zhaodelong/restaurant
Laioffer Project Class
zhaodelong/RSA-example
A python SAGE code to show the basic RSA encryption and decription
zhaodelong/scikit-learn
scikit-learn: machine learning in Python
zhaodelong/TripTracker
zhaodelong/TryDjango18
zhaodelong/tryflask
Learn flask from http://flask.pocoo.org/
zhaodelong/wechat_jump_hack
腾讯微信跳一跳破解（目前最高19844分）
zhaodelong/zhaodelong.github.io
zhaodelong/zhuaxia
download mp3 files/albums from xiami.com and music.163.com