All Projects â†’ PRML â†’ Prmlt

PRML / Prmlt

Licence: mit
Matlab code of machine learning algorithms in book PRML

Programming Languages

matlab
3953 projects

Projects that are alternatives of or similar to Prmlt

Pyltr
Python learning to rank (LTR) toolkit
Stars: ✭ 377 (-92.96%)
Mutual labels:  machine-learning-algorithms
Ml Roadmap
🤖 Roadmap to becoming a Machine Learning developer in 2020
Stars: ✭ 398 (-92.57%)
Mutual labels:  machine-learning-algorithms
Learn Data Science For Free
This repositary is a combination of different resources lying scattered all over the internet. The reason for making such an repositary is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. For Constant Updates Follow me in …
Stars: ✭ 4,757 (-11.18%)
Mutual labels:  machine-learning-algorithms
Onedal
oneAPI Data Analytics Library (oneDAL)
Stars: ✭ 382 (-92.87%)
Mutual labels:  machine-learning-algorithms
Data Science And Machine Learning From Scratch
Implements common data science methods and machine learning algorithms from scratch in python. Intuition and theory behind the algorithms is also discussed.
Stars: ✭ 387 (-92.77%)
Mutual labels:  machine-learning-algorithms
Moa
MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
Stars: ✭ 409 (-92.36%)
Mutual labels:  machine-learning-algorithms
Differentiable Plasticity
Implementations of the algorithms described in Differentiable plasticity: training plastic networks with gradient descent, a research paper from Uber AI Labs.
Stars: ✭ 371 (-93.07%)
Mutual labels:  machine-learning-algorithms
Hyperparameter Optimization Of Machine Learning Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
Stars: ✭ 516 (-90.37%)
Mutual labels:  machine-learning-algorithms
Pattern classification
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
Stars: ✭ 3,880 (-27.56%)
Mutual labels:  machine-learning-algorithms
Datascience Ai Machinelearning Resources
Alex Castrounis' curated set of resources for artificial intelligence (AI), machine learning, data science, internet of things (IoT), and more.
Stars: ✭ 414 (-92.27%)
Mutual labels:  machine-learning-algorithms
Cudf
cuDF - GPU DataFrame Library
Stars: ✭ 4,370 (-18.41%)
Mutual labels:  machine-learning-algorithms
100daysofmlcode
Stars: ✭ 387 (-92.77%)
Mutual labels:  machine-learning-algorithms
Machinejs
[UNMAINTAINED] Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml
Stars: ✭ 412 (-92.31%)
Mutual labels:  machine-learning-algorithms
Lightml.jl
Minimal and clean examples of machine learning algorithms implemented in Julia
Stars: ✭ 382 (-92.87%)
Mutual labels:  machine-learning-algorithms
Ofxdarknet
darknet neural network addon for openFrameworks
Stars: ✭ 493 (-90.8%)
Mutual labels:  machine-learning-algorithms
Perceptron
A flexible artificial neural network builder to analyse performance, and optimise the best model.
Stars: ✭ 370 (-93.09%)
Mutual labels:  machine-learning-algorithms
Algorithmsanddatastructure
Algorithms And DataStructure Implemented In Python & CPP, Give a Star 🌟If it helps you
Stars: ✭ 400 (-92.53%)
Mutual labels:  machine-learning-algorithms
Solid
🎯 A comprehensive gradient-free optimization framework written in Python
Stars: ✭ 546 (-89.81%)
Mutual labels:  machine-learning-algorithms
Machine Learning Articles
Monthly Series - Top 10 Machine Learning Articles
Stars: ✭ 516 (-90.37%)
Mutual labels:  machine-learning-algorithms
Agile data code 2
Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition
Stars: ✭ 413 (-92.29%)
Mutual labels:  machine-learning-algorithms

Introduction

This Matlab package implements machine learning algorithms described in the great textbook: Pattern Recognition and Machine Learning by C. Bishop (PRML).

It is written purely in Matlab language. It is self-contained. There is no external dependency.

Note: this package requires Matlab R2016b or latter, since it utilizes a new Matlab syntax called Implicit expansion (a.k.a. broadcasting). It also requires Statistics Toolbox (for some simple random number generator) and Image Processing Toolbox (for reading image data).

Design Goal

  • Succinct: The code is extremely compact. Minimizing code length is a major goal. As a result, the core of the algorithms can be easily spotted.
  • Efficient: Many tricks for speeding up Matlab code are applied (e.g. vectorization, matrix factorization, etc.). Usually, functions in this package are orders faster than Matlab builtin ones (e.g. kmeans).
  • Robust: Many tricks for numerical stability are applied, such as computing probability in logrithm domain, square root matrix update to enforce matrix symmetry\PD, etc.
  • Readable: The code is heavily commented. Corresponding formulas in PRML are annoted. Symbols are in sync with the book.
  • Practical: The package is not only readable, but also meant to be easily used and modified to facilitate ML research. Many functions in this package are already widely used (see Matlab file exchange).

Installation

  1. Download the package to a local folder (e.g. ~/PRMLT/) by running:
git clone https://github.com/PRML/PRMLT.git
  1. Run Matlab and navigate to the folder (~/PRMLT/), then run the init.m script.

  2. Run some demos in ~/PRMLT/demo folder. Enjoy!

FeedBack

If you find any bug or have any suggestion, please do file issues. I am graceful for any feedback and will do my best to improve this package.

License

Released under MIT license

Contact

sth4nth at gmail dot com

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].