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Mlpack: Scalable C + + machine learning Library (RPM)

Summary:Mlpack is a scalable C + + machine learning library designed to allow new users to use machine learning with simple, consistent APIs while providing professional users with high performance and maximum flexibility in C + +

Shark: Powerful open-source C ++ Machine Learning Library

Shark is a fast, modular, and rich open-source C ++ Machine Learning Library. It provides various machine learning-related technologies, such as linear/nonlinear optimization and kernel-based learning.AlgorithmAnd neural networks. Shark has been applied to multiple real-worl

Deepdetect Machine learning Caffe and Xgboost API interface written with c++11

Https://github.com/beniz/deepdetectDeepdetect (http://www.deepdetect.com/) is a machine learning APIs and server written in C++11. It makes state of the "Art machine" learning easy-to-work with and integrate into existing applications.Deepdetect relies on external

C + + machine learning Classic Material

Caffe: A fast neural network frameworkAddress: Https://github.com/BVLC/caffeCCV: A modern computer vision library with C language as its coreAddress: HTTPS://GITHUB.COM/LIULIU/CCVMlpack: Extensible C + + machine learning LibraryAddress: https://github.com/anticlockwOpenCV: Open Source computer Vision LibraryAddress: HT

Classification of machine learning algorithms based on "machine Learning Basics"--on how to choose machine learning algorithms and applicable solutions

written text can really deepen the understanding of the problem, and constantly self-thinking. After all, I write these things not for the sake of books, but to accumulate the key content of learning flexibly, and to do better knowledge management. Of course, it would be better if it helped the reader.Reprint please indicate the author Jason Ding and its provenanceGitHub home page (http://jasonding1354.github.io/)CSDN Blog (http://blog.csdn.net/jason

C + + code of perceptual machine in statistical learning

Perceptron is an ancient statistical learning method, which is mainly applied to two types of linear data, and the strategy is to correct the error points on a given super-plane so that all points are correctly divided.The method used is the stochastic gradient descent method, which is linear and can guarantee the final convergence in finite step. Specific reference to Hangyuan Li's "Statistical learning me

Robot Learning Cornerstone Machine Learn Cornerstone (machines learining foundations) Job 2 q16-18 C + + implementation

Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning Foundations)-Job 2 q16-18 C + + implementation. Although there are many great gods in many blogs have given the implementation of Phython, but given the C

C # Learning record 10--on-machine experiment results

link libraryand adding using Microsoft.Phone.Scheduler;Below, just when you think everything is perfect ... The problem arises:There are many places on the internet where the problem is nowhere to be found. Finally, a post on MSDN found the problem.Reference URL: https://social.msdn.microsoft.com/Forums/en-US/68b50bbb-3296-46be-a85f-669caecedd19/ Wp81-no-alarms-reminders-whats-the-alternative?forum=wpdevelopThe explanation here says:WP8.1 alarms and reminders are no longer available to third-pa

Deep Learning Book recommendation, deep learning book

Deep Learning Book recommendation, deep learning bookAI Bible Classic best-selling book in the field of deep learning! Has long ranked first in Amazon AI and machine learning boo

Machine learning and its application 2013, machine learning and its application 2015

Machine learning and its application 2013 content introduction BooksComputer BooksMachine learning is a very important area of research in computer science and artificial intelligence. In recent years, machine learning has not only been a great skill in many fields of comput

Principle and programming practice of machine learning algorithm Chapter One basics of machine learning __ Machine learning

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us int

"Machine Learning Series" New Lindahua recommended Books for the machine learning community

, you understand what is the factors that might influence the run-time performance of your C Odes.CUDA programming:a Developer ' s Guide to Parallel Computing with GPUsShane CookThis book provides an in-depth coverages of important aspects related to CUDA programming – a programming technique that C An unleash the unparalleled power of GPU computation. With CUDA

The best introductory Learning Resource for machine learning

. Overwhelmed by machine Learning:is there a ML101 book: This is a problem on the StackOverflow. A range of machine learning recommended books are available. The first answer that Jeff Moser provides is useful, including links to course videos and lectures. Related theories, books, papers,

"Machine Learning Basics" machine learning Cornerstone Course Learning Introduction

learning to organize the daily learning of machine learning algorithms, and practical problems, do more experiments, and strive to get a better learning effect, I will be firm belief, more efforts to catch up with the pace of excellence.Reprint please indicate the author Ja

About the technical flaws in the book "C # IoT Programming Fundamentals", if you are going to read a book, please come in and see!

width is generally not height, but with MinHeight, maxheight to define the minimum height, the maximum height, so that the control to achieve a different window size of the adaptive-this is the WPF writing interface advantages, and the author abandoned, With WinForm thinking to achieve, interface code is flooded with a lot of margin is also the embodiment of WinForm thinking.Perhaps the author explains: the introduction of a large number of WPF features increases the cost of

Stanford Machine Learning---the eighth lecture. Support Vector Machine Svm_ machine learning

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

Machine learning-Hangyuan Li-Statistical Learning Method Learning Note perception Machine (2)

wrong classification point is not, then the value of the loss function is definitely 0.The Perceptual machine learning algorithm is driven by mis-classification and adopts random gradient descent method. First, arbitrarily select a super-planar w,b and then minimize the target function. The definitions are given in the author's book. Not a wordy.The original for

Stanford Machine Learning---The sixth week. Design of learning curve and machine learning system

sixth week. Design of learning curve and machine learning system Learning Curve and machine learning System Design Key Words Learning curve, deviation variance diagnosis method, error a

Learning notes for "Machine Learning Practice": Implementation of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k-

Learning notes for "Machine Learning Practice": Implementation of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- The main learning and research tasks of the last semester were pattern recognition, signal theor

[Deep Learning a MIT press book in preparation] Deep Learning for AI

Moving DL we have six months of time, accumulated a certain experience, experiments, also DL has some of their own ideas and understanding. Have wanted to expand and deepen the DL related aspects of some knowledge.Then saw an MIT press related to the publication DL book http://www.iro.umontreal.ca/~bengioy/dlbook/, so you have to read this book and then make some notes to save some knowledge of the idea. Th

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