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Name: Clusters
Type: User
Company: Clusters
Bio: Clusters
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Blog: Clusters
Name: Clusters
Type: User
Company: Clusters
Bio: Clusters
Location: Clusters
Blog: Clusters
Criminology
Criminology
Criminology
Cross-stitch.
Crypto Columns
GATech OMSCS Machine Learning Course -- notes and assignments
CS229 Machine Learning open course by Andrew Ng
Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".
Machine Learning code for CS7641
CuckooML: Machine Learning for Cuckoo Sandbox
Curbing City Violence
Curbing City Violence
C++ framework for realtime machine learning.
Darks learning is the machine learning algorithm library. It contains Word2vec,DBN, RBM, MLP, LSA, PLSA, SDA, Maxent, regression, etc.
Dart-a-Mania
Distributed and parallel machine learning
Repository of teaching materials, code, and data for my data analysis and machine learning projects.
Data Mining?
Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques
An open source book to learn data science, data analysis and machine learning, suitable for all ages!
Ways of doing Data Science Engineering and Machine Learning in R and Python
data switching
Complete machine learning model codes
程序猿的数据科学与机器学习实战手册
Data Science Specialization Course
A collection of my notes and information that I came across while exploring Machine Learning and Data Science. This information is primarily targeted at my meetup group - Dublin Data Science Beginners. If you are in Dublin, I would like to extend an invitation to attend one of the meetup sessions. Good luck with your learning.
common data analysis and machine learning tasks using python
a curated list of R tutorials for Data Science, NLP and Machine Learning
Public Machine Learning and Data Competition Repo
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.