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Name: Andy Yuan
Type: User
Company: Nortel->Ericssion->Cisco->Startup
Name: Andy Yuan
Type: User
Company: Nortel->Ericssion->Cisco->Startup
Deep Learning and applications in Startups, CV, Text Mining, NLP
List of awesome university courses for learning Computer Science!
A curated list of awesome Flask resources and plugins
Indexes fields in a CSV file using a bloom filter probablistic set structure.
some books
Easy benchmarking of all public open-source implementations of convnets
Public facing notes page
Modelled, Architected and designed by Vance King Saxbe. A. with the geeks from GoldSax Consulting, GoldSax Money, GoldSax Treasury, GoldSax Finance, GoldSax Banking and GoldSax Technologies email @[email protected]. Development teams from Power Dominion Enterprise, Precieux Consulting. This Engagement sponsored by GoldSax Foundation, GoldSax Group and executed by GoldSax Manager. By Ensemble methods, Combining multiple classifiers exploits the shortcomings of single classifiers, such as overfitting. Combining multiple classifiers help, as long as the classifiers are significantly different from each other. This difference is in the algorithm or in the data applied to that algorithm. The two types of ensemble methods bagging and boosting. In bagging, datasets the same size as the original dataset are built by randomly sampling examples for the dataset with replacement. Boosting takes the idea of bagging a step further by applying a different classifier sequentially to a dataset. AdaBoost uses a weak learner as the base classifier with the input data weighted by a weight vector. In the first iteration the data is equally weighted. But in subsequent iterations the data is weighted more strongly if it was incorrectly classified previously. This adapting to the errors is the strength of AdaBoost. We built functions to create a classifier using AdaBoost and the weak learner, decision stumps. The AdaBoost functions can be applied to any classifier, as long as the classifier can deal with weighted data. The AdaBoost algorithm is powerful, and it quickly handled datasets that were difficult using other classifiers. The classification imbalance problem is training a classifier with data that doesn’t have an equal number of positive and negative examples. The problem also exists when the costs for misclassification are different from positive and negative examples. We looked at ROC curves as a way to evaluate different classifiers. We use precision and recall as metrics to measure the performance classifiers when classification of one class is more important than classification of the other class. We use oversampling and undersampling as ways to adjust the positive and negative examples in a dataset.dealing with classifiers with unbalanced objectives. This method takes the costs of misclassification into account when training a classifier.
Java for Android
an implementation of latent Dirichlet allocation (LDA) with stochastic variational inference
A List of Recommender Systems and Resources
Classical equations and diagrams in machine learning
Machine Learning / Natural Language Processing / Randomized Algorithm
Markyun在GitHub上的文字记录
Learn machine learning for free, because free is better than not-free.
Must-watch videos about Python
Statistical arbitrage simulation, modeling and backtesting with Python.
ctp wrapper for python
Useful functions, tutorials, and other Python-related things
Materials for my Pycon 2015 scikit-learn tutorial.
Scikit-Learn Tutorial for PyData Seattle 2015
A curated list of speech and natural language processing resources
the src of willey_py_ML
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
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.