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3 Types of Learning3.1 Learning with Different Output Space YThe method of machine learning is categorized from the angle of the output spatial type.1. Two-dollar classification (binary classification): The output label is discrete, two-class.2. Multivariate classification (Multiclass classification): The output label is discrete, multi-class. The dualistic classification is a special case of multivariate c
Aimless search for some things, found a good resource, so it must be collected, easy to learn Linux C when you can also refer to other people's learning path, to promote my study and thinkingDescriptionReprint please specify the source: Thank you: http://blog.csdn.net/muge0913/article/details/7342977Blogger's email address is: [Email protected]If there are incorrect or some functions in the article to achieve a better way, please indicate or direct me
This article is part of the third chapter of "Neural networks and deep learning", which describes how to select the value of the initial hyper-parameter in the machine learning algorithm. (This article will continue to add)Learning Rate (learning rate,η)When using the gradient descent algorithm to optimize, the weight
Deep Learning thesis notes (8) Latest deep learning Overview Zouxy09@qq.com Http://blog.csdn.net/zouxy09 I have read some papers at ordinary times, but I always feel that I will slowly forget it after reading it. I did not seem to have read it again one day. So I want to sum up some useful knowledge points in my thesis. On the one hand, my understanding will be deeper, and on the other hand, it will facili
Programming Java Learning Path (i), tools One, JDK (Java Development Kit) The JDK is the core of the entire Java system, including the Java Runtime Environment (Java Runtime envirnment), a stack of Java tools and a Java-based class library (Rt.jar). No matter what Java application Server is in essence a version of the JDK is built in. So mastering the JDK is the first step in learning java. The most main
Machine learning Types Machine Learning Model Evaluation steps Deep Learning data Preparation Feature Engineering Over fitting General process for solving machine learning problems Machine Learning Four BranchesThe second classification, multi-classi
Original address:http://blog.csdn.net/miscclp/article/details/6339456Under the traditional machine learning framework, the task of learning is to learn a classification model based on a given sufficient training data, and then use this learning model to classify and predict the test document. However, we see that the machine
Multi-View Learning ( Multi-View Learning )Early bragging: Today this chapter we are to brag about, just started the boss and I said what is called multi-view learning, my mind is so understanding: we are in the picture of sister welfare, not only to see $ degree angle of the bar, or that would not be all beautiful, this also got. So we have to look at various a
Deep learning is now a hot concept in machine learning, but the concept has become a bit of a myth as it is reproduced in various media: for example, deep learning can be thought of as a machine learning method that simulates the neural structure of the human brain, thus enabling the computer to have the same intellige
Today I am honored to have the opportunity to share with you the topic of enhanced learning (reinforcement LEARNING,RL). This time, I hope to achieve the goal of three aspects: First, I hope that no relevant background of the students can have a certain understanding of RL, so I will introduce some basic concepts. Second, I hope that students with the background of machine
Programming Libraries Programming Library ResourcesI am an advocate of the concept of "learning to be adventurous and try." This is the way I learn programming, I believe many people also learn to program design. First understand your ability limits, then expand your ability. If you know how to program, you can draw on the experience of programming quickly to learn more about machine learning. Before you im
Reading List List of reading lists and survey papers:BooksDeep learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press, in preparation.Review PapersRepresentation learning:a Review and New perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent, ARXIV, 2012. The monograph or review paper Learning deep architectures for AI (Foundations Trends in Machine
In fact, there are many ways to learn about machine learning and many resources such as books and open classes. Some related competitions and tools are also a good helper for you to understand this field. This article will focus on this topic, give some summative understanding, and provide some learning guidance for the transformation from programmers to machine learnin
Under the traditional machine learning framework, the task of learning is to learn a classification model based on a given sufficient training data, and then use this learning model to classify and predict the test document. However, we see that the machine learning algorithm has a key problem in the current research o
The basic thought of deep learningSuppose we have a system s, which has n layers (S1,... SN), its input is I, the output is O, the image is expressed as: I =>S1=>S2=>.....=>SN = o, if the output o equals input I, that is, input I after this system changes without any information loss (hehe, Daniel said, it is impossible.) In the information theory, there is a "message-by-layer-loss" statement (processing inequalities), the processing of a information obtained B, and then the B processing to get
Statement: This blog post according to Http://www.ctocio.com/hotnews/15919.html collation, the original author Zhang Meng, respect for the original.Machine learning is undoubtedly a hot topic in the field of current data analysis. Many people use machine learning algorithms more or less in their usual work. This article summarizes common machine learning algori
TensorFlow integrates and implements a variety of machine learning-based algorithms that can be called directly.Supervised learning1) Decision Trees (decision tree)Decision tree is a tree structure, providing people with decision-making basis, decision tree can be used to answer yes and no problem, it through the tree structure of the various situations are represented, each branch represents a choice (select Yes or no), until all the choices are fini
How to select Super Parameters in machine learning algorithm: Learning rate, regular term coefficient, minibatch sizeThis article is part of the third chapter of "Neural networks and deep learning", which describes how to select the value of the initial hyper-parameter in the machine learning algorithm. (This article w
The inverse propagation algorithm (back-propagtion algorithm), BP learning is a supervised learning algorithm, which is an important method of artificial neural network learning, which is often used to train feedforward multilayer perceptron neural networks.First, the principle of BP learning1. Feed-forward neural networkRefers to the network in the processing of
11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data 11.1 what to do firstIn the next video, I'll talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when designing a complex machine learning s
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