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
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
This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This course is a choice for those who want to understand and master machine learning. This course covers some of the basic concepts and methods of machine
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 learning parameters and we're going to test our hypothet
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 a great advantage: the operation speed is very fast, accurate rate is high, can online se
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
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
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
Learning the learning notes series of OpenCV (2) source code compilation and sample projects, opencv learning notesDownload and install CMake3.0.1
To compile the source code of OpenCV2.4.9 by yourself, you must first download the compilation tool. CMake is the most widely used compilation tool.
The following is an introduction to CMake:
CMake is a cross-platform
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
Bengio, LeCun, Jordan, Hinton, Schmidhuber, Ng, de Freitas and OpenAI had done Reddit AMA's. These is nice places-to-start to get a zeitgeist of the field.Hinton and Ng lectures at Coursera, UFLDL, cs224d and cs231n at Stanford, the deep learning course at udacity, and the sum Mer School at IPAM has excellent tutorials, video lectures and programming exercises that should help you get STARTED.NB Sp The online book by Nielsen, notes for cs231n, and blo
In general, the relationship between recall and precision is as follows:1, if the need for a high degree of confidence, the precision will be very high, the corresponding recall rate is very low, 2, if the need to avoid false negative, the recall rate will be very high, the precision will be very low. on the right, the relationship between recall rate and precision ratio is shown in a learning algorithm. It is important to note that no
Chapter I. Introduction to Statistical learning methodsThe main features of statistical learning are:
(1) Statistical learning is based on computers and networks, and is based on computer and network
;
(2) Statistical learning takes data as the research object and is a data-driven discipline;
(3) Un
1. Write in frontSupervised learning (supervised learning), unsupervised learning (unsupervised learning), and semi-supervised learning (semi-supervised learning) in the field of machine learn
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
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 into the world of machine learning!
1.1 Program
Python vector:
Import NumPy as np
a = Np.array ([[[1,2],[3,4],[5,6]])
SUM0 = Np.sum (A, axis=0)
sum1 = Np.sum (A, Axis=1)
PR int SUM0
Print sum1
> Results:
[9 12][3 7] Dropout
In the training process of the deep Learning Network, for the Neural network unit, it is temporarily discarded from the network according to certain probability.Dropout is a big kill for CNN to prevent the effect of fitting. Output is 10 categories, so the dimension is 10
Mod
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. This series of blog will be note-type, what is bad to write about the vast number of Bo frie
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