where to start machine learning

http://blog.csdn.net/pipisorry/article/details/44904649Machine learning machines Learning-andrew NG Courses Study notesLarge Scale machines Learning large machine learningLearning with Large datasets Big Data Set LearningStochastic Gradient descent random gradient descentMini-batch Gradient descent mini batch processin

Students in the field of machine learning know that there is a universal theorem in machine learning: There is no free lunch (no lunch). The simple and understandable explanation for it is this: 1, an algorithm (algorithm a) on a specific data set than the performance of another algorithm (algorithm B) at the same ti

The method of Ascension is to start from the weak learning algorithm, to learn, to get a series of weak classifier (basic classifier), and then combine these weak classifiers, build a strong classifier. Most of the lifting methods change the probability distribution (weight distribution) of training data, call the weak learning algorithm according to different tr

We will learn how to systematically improve machine learning algorithms, tell you when the algorithm is not doing well, and describe how to ' debug ' your learning algorithms and improve their performance "best practices". To optimize machine learning algorithms, you need to

investigate.7.2 Error AnalysisThe recommended Practices for solving machine learning problems are: Start with a simple algorithm the can implement quickly. Implement it and test it on your cross-validation data. Plot Learning curves to decide if more data, more features, etc. is likely to help. The Er

deep understanding of machine learning: Learning Notes from principles to algorithms-1th week 02 easy to get started Deep understanding of machine learning from principle to algorithmic learning notes-1th week 02 Easy to get star

The motive and application of machine learningTools: Need genuine: Matlab, free: Octavedefinition (Arthur Samuel 1959):The research field that gives the computer learning ability without directly programming the problem.Example: Arthur's chess procedure, calculates the probability of winning each step, and eventually defeats the program author himself. (Feel the idea of using decision trees)definition 2(Tom

is suffering from the high variance, the Getting more training data are likely to help. A neural Neural network with fewer parameters are prone to underfitting. It is also computationally cheaper. A large neural network with more parameters are prone to overfitting. It is also computationally expensive. Machine learning System desing: The recommended approach:

If we are developing a machine learning system and want to try to improve the performance of a machine learning system, how do we decide which path we should choose Next?In order to explain this problem, to predict the price of learning examples. If we've got the

Preface: Today just heard a talk about Extreme learning Machine (Super limited learning machine), the speaker is Elm Huangguang Professor . The effect of elm is naturally much better than the SVM,BP algorithm. and relatively than the current most fire deep learning, it has

Forecast for 2018 machine learning conferences and 200 machine learning conferences worth attention in 200 2017 is about to pass. How is your harvest this year? In the process of learning, it is equally important to study independently and to learn from others. It is a goo

four concepts and ideas that I think are extremely important, there are some main contents such as: the excessive use of VC dimension,noise and limited data size N, several methods to solve the overfitting, overfitting Tip: Validation (cross Validation,leave one out validation, N-folder valiation ... ), data hinting, data cleaning/pruning, regularization, start from the simple model, and so on. This is no longer a summary.****************************

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

Source: https://www.cnblogs.com/jianxinzhou/p/4083921.html1. The problem of overfitting (1) Let's look at the example of predicting house price. We will first perform linear regression on the data, that is, the first graph on the left. If we do this, we can obtain such a straight line that fits the data, but in fact this is not a good model. Let's look at the data. Obviously, as the area of the house increases, the changes in the housing price tend to be stable, or the more you move to the right

Application Recommendations for machine learningFor a long time, the machine learning notes have not been updated, the last part of the updated neural network. This time we'll talk about the application of machine learning recommendations.Decide what to do nextSuppose we nee

A conceptual atlas of machine learning Second, what is machine learning Machine learning (machine learning) is a recent hot field, about so

1. Google Cloud Machine learning Platform Introduction:The three elements of machine learning are data sources, computing resources, and models. Google has a strong support in these three areas: Google not only has a rich variety of data resources, but also has a strong computer group to provide data storage in the dat

and then start using them in the application. In addition, many machine learning models and training data have been released by other research institutions and universities, which are often not published in the Core ML Model format. If you want to use these models, you need to convert them. For details, see "Convert trained models to Core ML 」. Integrate the Co