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The learning experience of statistical learning method (Hangyuan Li) (i.)

A blink of an eye, from the beginning of contact with machine learning, to now be trivial about, have to put down Hangyuan Li Teacher's "statistical learning method", nearly five months. For five months, the first one months were the happiest of my time, and it was wonderful to enjoy all the thinking that the statistical learning method brings. By the end of the

Python machine learning: 6.3 Debugging algorithms using learning curves and validation curves

In this section we learn two very useful diagnostic methods that can be used to improve the performance of the algorithm. They are the learning curve (learning curve) and the validation curve (validation curve). The learning curve can be used to determine whether the learning algorithm is over-fitted or under-fitted.Us

20165334 Learning Basics and C language learning experience

Learning basic and C language learning experience one, skills learning?? I think in the boys, I should be good at cooking. I was in grade four began to learn to cook, from the beginning of learning to cook rice, to later steamed buns, to the last stir-fried home cooking really has a sense of accomplishment. First of al

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 (

Unsupervised learning features-Sparse Coding, deep learning, and ICA represent one of the documents

Reproduced http://blog.csdn.net/zhoutongchi/article/details/8191991 Learning ing functions and literature applied in behavior recognition/image classification (models and non-models are associated with each other, and algorithms are mutually adopted. There is no clear distinction between them, including the bionic literature) %The research focuses on ICA model and deep learning with sparse encoding. 1) spar

Unity learning Summary 4. unity learning Summary

Unity learning Summary 4. unity learning Summary Silence for a long time, busy for a long time, starting from the beginning is still trying to find excuses for failing to make a good summary and thinking. To sum up, time can still be drawn out. Recently, the accumulation of pitfalls seems to be almost enough. This is also equivalent to leaving more attention to solving problems in the future. I hope you can

Deep Learning Series-Preface: A good tutorial for deep learning

Written before: busy, always in a walk stop, squeeze time, leave a chance to think. Intermittent, the study of deep learning also has a period of time, from the beginning of the small white to now is a primer, halfway to read a little article literature, there are many problems. The trip to Takayama has only just begun, and this series is designed to record the path and individual learning sentiment

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

Learning programming, first figuring out what learning is?

Let's take a look at some historical explanations:First, describe the meaning of words.In ancient China, learning and learning are always separated. The "Ci yuan" pointed out that "Learning" is "imitating", that is, acquiring knowledge; "Learning" is "reviewing" and "Practicing", that is, reviewing and consolidating. C

Science: About machine learning--talking from machine learning

Source: From Machine learningThis paper first introduces the trend of Internet community and machine learning Daniel, and the application of machine learning, then introduces the machine learning with a "et story". The first is the concept and definition of machine learning, then the related disciplines of machine

Web Front-end development learning routes and web development learning routes

Web Front-end development learning routes and web development learning routes Lead: first, I will share my experience. to do one thing well, I have to spend some time, and then I will learn more, think more, practice more, communicate more, and summarize more to find my own problems, then we must overcome this problem. When the status is poor, we must adjust it in time. If you are a beginner, you must think

Machine learning--Neighbor Component Analysis (NCA) algorithm and Metric learning

1. Nearest Neighbor Component analysis (NCA) algorithmAbove content reproduced from: http://blog.csdn.net/chlele0105/article/details/130064432. Metric LearningIn machine learning, the main purpose of dimensionality reduction of high dimensional data is to find a suitable low-dimensional space, in which the learning can be better than the original space performance. Each space corresponds to a distance metri

"Machine learning" describes a variety of dimensionality reduction algorithms _ Machine learning Combat

information table, X indicates that the dimensions of the high dimensional input matrix are the high dimension D times the number of samples N, C=xxt, Z represents the dimension reduction output matrix size low dimension d times N, E=zzt, the linear mapping is Z=WTX, the distance matrix between 22 in the high-dimensional space is a, and the SW,SB is LDA respectively. In-class divergence matrices and inter-class divergence matrices, K indicates that a point in manifold

Stanford University public Class machine learning: Machines Learning System Design | Error metrics for skewed classes (definition of skew class issues and evaluation measures for skew class issues: precision ratio (precision) and recall rate (recall))

The previous article mentioned the importance of error analysis and setting error metrics. That is to set a real number to evaluate the learning algorithm and measure its performance. With the evaluation and error metrics of the algorithm, one important thing to note is that using an appropriate error metric can sometimes have a very subtle effect on the learning algorithm. This kind of problem is the probl

. 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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