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Linux learning materials, so learning Linux more

The first thing to think about is what to solve, the most important of which are three aspects: efficiency, scale, and some intrinsic requirements of machine learning itself.ScaleThe so-called scale problem has three points. The first is that the volume of data is growing rapidly, with more than 60% growth in public cloud and video data each year. 2nd, the amount of data is very large, such as seven cattle have 200 billion pictures, more than 1 billio

A picture of the difference between AI, machine learning and deep learning

Turn from 70271574AI (AI) is the future, is science fiction, is part of our daily life. All the assertions are correct, just to see what you are talking about AI in the end.For example, when Google DeepMind developed the Alphago program to defeat the Korean professional Weiqi master Lee Se-dol, the media in the description of the victory of DeepMind used AI, machine learning, deep learning and other terms.

Supervised learning and unsupervised learning

The common methods of machine learning are mainly divided into supervised learning (supervised learning) and unsupervised learning (unsupervised learning).Supervised learning, which is often said to be classified , is trained to o

Comprehensive learning path–data Science in Python deep learning path-Learn with Python data

http://blog.csdn.net/pipisorry/article/details/44245575A very good article on how to learn python and use Python for data science, data analysis, machine learning Comprehensive learning Path–data Science in PythonDeep learning paths-data learning with PythonJourney from a pythonnoob(Novice) to a kaggler on PythonSo,

(note) Stanford machine Learning--generating learning algorithms

Contents of this lecture1. Generative Learning algorithms (Generate learning Algorithm)2. GDA (Gaussian discriminant analysis)3. Naive Bayes (Naive Bayes)4. Laplace Smoothing (Laplace smoothing)1. Generate learning Algorithms and discriminant learning algorithmsDiscriminant Learnin

Notes of machine Learning (Stanford), Week 6, Advice for applying machine learning

This paper uses the regularization linear regression model pre-flow (water flowing out of dam) according to the water storage line (water level) of the reservoir, then the Debug Learning Algorithm and discusses the influence of deviation and variance on the linear regression model.① visualizing datasetsThe data set for this job is divided into three parts:Training set (training set), sample matrix (Training Set): X, results label (label of result) Vec

Machine learning and artificial Intelligence Learning Resource guidance

Machine learning and artificial Intelligence Learning Resource guidanceToplanguage (https://groups.google.com/group/pongba/)I often recommend some books in the toplanguage discussion group, and often ask the cows inside to gather some relevant information, artificial intelligence, machine learning, natural language processing, knowledge discovery (especially, dat

Coursera open course notes: "Advice for applying machine learning", 10 class of machine learning at Stanford University )"

Stanford University machine Learning lesson 10 "Neural Networks: Learning" study notes. This course consists of seven parts: 1) Deciding what to try next (decide what to do next) 2) Evaluating a hypothesis (Evaluation hypothesis) 3) Model selection and training/validation/test sets (Model selection and training/verification/test Set) 4) Diagnosing bias vs. variance (diagnostic deviation and variance) 5) Reg

Deep reinforcement learning--dqn_ depth Learning

Contact Way: 860122112@qq.com DQN (Deep q-learning) is a mountain of deep reinforcement learning (Deep reinforcement LEARNING,DRL), combining deep learning with intensive learning to achieve from perception (perception) to action (action ) is a new algorithm for End-to-end (

Deep Learning Series (V): A simple deep learning toolkit

This section mainly introduces a deep learning MATLAB version of the Toolbox, Deeplearntoolbox The code in the Toolbox is simple and feels more suitable for learning algorithms. There are common network structures, including deep networks (NN), sparse self-coding networks (SAE), CAE, depth belief networks (DBN) (based on Boltzmann RBM implementations), convolutional neural Networks (CNN), and so on. Thanks

The path to Hadoop learning (i)--hadoop Family Learning Roadmap

The main introduction to the Hadoop family of products, commonly used projects include Hadoop, Hive, Pig, HBase, Sqoop, Mahout, Zookeeper, Avro, Ambari, Chukwa, new additions include, YARN, Hcatalog, O Ozie, Cassandra, Hama, Whirr, Flume, Bigtop, Crunch, hue, etc.Since 2011, China has entered the era of big data surging, and the family software, represented by Hadoop, occupies a vast expanse of data processing. Open source industry and vendors, all data software, no one to Hadoop closer. Hadoop

Writing machine learning from the perspective of Software Project Project analysis of main supervised learning algorithms in 3--

Project applicability analysis of main machine learning algorithmsSome time ago Alphago with the Li Shishi of the war and related deep study of the news brush over and over the circle of friends. Just this thing, but also in the depth of machine learning to further expand, and the breadth of machine learning (also known as Project practice), there is still no bre

Learning sort Learning to Rank summary

Learning sort (learning to Rank) LTR (learning torank) Learning sequencing is a sort of supervised learning (supervisedlearning) method. LTR has been widely used in many fields of text mining, such as the documents returned in IR, the candidate products in the recommendation

Recommending music on Spotify and deep learning uses depth learning algorithms to make content-based musical recommendations for Spotify

This article refers to http://blog.csdn.net/zdy0_2004/article/details/43896015 translation and the original file:///F:/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9% A0/recommending%20music%20on%20spotify%20with%20deep%20learning%20%e2%80%93%20sander%20dieleman.htmlThis article is a blog post by Dr. Sander Dieleman, Reservoir Lab Laboratory at the University of Ghent (Ghent University) in Belgium, where his research focuses on the classification of Music audio signals and the recommended hierarchical charac

On manifold learning (manifold learning)

Machine learning Although the name took learning a word, let a person at first glance feel compared with Intelligence is just a change of argument, but in fact here the meaning of learning is much simpler. Let's take a look at the typical process of machine learning, which sometimes feels like applying math or more pop

[Reading Notes-learning methods] "The art of deep learning"-copper mining

He admired the bronze teacher for a long time, and when he learned that he had written a book on learning methods, "The art of deep learning", he bought the first ebook I paid for in my life on the Amazon China website.This reading note is not exactly in accordance with the original book narrative sequence excerpt, but through my modification and collation.Reading Note text:The so-called deep

Getting Started with machine learning-understanding machine learning + Simple perceptron (Java implementation)

First, let's talk about gossip.  If you go to machine learning now, will you go? Is it because you are not interested in this aspect, or because you think this thing is too difficult, you will not learn? If you feel too difficult, very good, believe that after reading this article, you will have the courage to step into the field of machine learning. Machine learning

Some learning methods under high-intensity learning of dark Horse

Through the teaching of multiple classes, as well as the exchanges with the students, found that many students learn bad, not learning, but not learning, which led to some students learn to struggle, and even pain, so based on personal ideas, the students learn to make some personal summary, hope to benefit everyone.The following learning methods are not for all

"Reprint" Learning Guide for machine learning beginners (experience sharing)

Learning Guide for machine learning beginners (experience sharing)2013-09-21 14:47I computer research two, the professional direction of natural language processing, individuals interested in machine learning, so began to learn. So, this guy is a rookie ... It is because of their own is a rookie, so realize the hardships of self-study machine

. NET learning route and various stages of learning books, blog posts, video sharing

This document was written by one of the major Java gods who wanted to learn. NET at level 15. I think, blog Park is the place where I grow and progress, as a Zhuang with the Internet to enjoy bi spirit of literary female youth, I should share it here to give more need to want to learn. NET children's shoes let them go to grow, let them less to learn some detours, write unreasonable place, welcome everyone criticize correct, or have better study suggestions and

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