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have been standing behind the scenes, and some things all the ins and outs only I know, because I and Dr. Huanghai, NetEase Cloud class, Professor Wunda and Coursera GTC translation platform, Deeplearning.ai official have had exchanges, so I still have to leave something as a description, Save everyone in the network every day noisy ah did not calm down to study seriously. As mentioned in this article, I have a chat record to support, some of the authorized information to retain the e-Mail recor
Naive BayesianThis course outline:1. naive Bayesian- naive Bayesian event model2. Neural network (brief)3. Support Vector Machine (SVM) matting – Maximum interval classifierReview:1. Naive BayesA generation learning algorithm that models P (x|y).Example: Junk e-mail classificationWith the mail input stream as input, the output Y is {0,1},1 as spam, and 0 is not j
Summary: What is data mining. What is machine learning. And how to do python data preprocessing. This article will lead us to understand data mining and machine learning technology, through the Taobao commodity case data preprocessing combat, through the iris case introduced a variety of classification algorithms.
Intr
area. Character segmentation: The task at this stage is to split the characters on the image of the license plate area into a separate image. Character Recognition: The task at this stage is to recognize the previously segmented character image as a specific character. At this stage we will use machine learning. enough theory, can you start coding now?
Of course
. Optimal interval classifierThe optimal interval classifier can be regarded as the predecessor of the support vector machine, and is a learning algorithm, which chooses the specific W and b to maximize the geometrical interval. The optimal classification interval is an optimization problem such as the following:That is, select Γ,w,b to maximize gamma, while satisfying the condition: the maximum geometry in
Java Virtual machine learning-in-depth understanding of the JVM (1)Java Virtual machine learning-slowly pondering the JVM (2)Java Virtual machine learning-slowly pondering the working mechanism of the JVM (2-1) ClassLoaderJava Vir
training on the basis of the known data samples, and the classification data model is used to predict the numerical data. Unsupervised learning is the clustering of data. Therefore, the main task of machine learning is classification.What issues do we need to consider when applying machine
predictions, and show results.
The advantage is that there are so many techniques and ways to do the same thing with this platform. In the second part you will find a simple or best practice to accomplish every subtask of a generic machine learning project. Here's a summary of the second part and a sub-task you can learn
Lesson one: The Python ecosystem for
Coursera Andrew Ng Machine learning is really too hot, recently had time to spend 20 days (3 hours a day or so) finally finished learning all the courses, summarized as follows:(1) Suitable for getting started, speaking the comparative basis, Andrew speaks great;(2) The exercise is relatively easy, but to carefully consider each English word, or easy to make mist
implied variables obtained by the E step.Repeat 2 steps above until convergence.The formula is as follows:The derivation process of the Nether function in M-Step formula:A common example of the EM algorithm is the GMM model, where each sample is likely to be produced by K-Gaussian, except that each Gaussian produces a different probability, so each sample has a corresponding Gaussian distribution (one of the k's), at which point the implied variable is a Gaussian distribution corresponding to e
, i.e. gt,i=gt,i+n (0,σ2t) The variance of the Gaussian error requires annealing: σ2t=η (1+t) γ increasing the random error on the gradient increases the robustness of the model, even if the initial parameter values are not chosen well and is suitable for training in a particularly deep-seated network. The reason for this is that increasing random noise is more likely to jump over local extreme points and find a better local extremum, which is more common in deep networks. Summary in the above
nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get Markov chainStep, set each word to a state, and then calculate the prob
Machine Learning notes of the Dragon Star program
Preface
In recent weeks, I spent some time learning the machine learning course of the Dragon Star program for the next summer vacation. For more information, see the appendix. Th
.6.2. Statistics on the degree of fire, that is, the amount and content of sharing.6.3. Explore how the fire, that is, to explore the characteristics of communication.6.4. Then build a predictive model of your own content to see if it will fire.6.5. Finally, a summary.7. Before using the logistic regression method to predict the IPO market, the machine learning is used to predict the market.7.1. First of al
python Programming
Huangge python Remote Video Training Course
Article/index. md at master · pythonpeixun/article · GitHub
Yellow brother python Training Workshop video playback address
Article/python_shiping.md at master · pythonpeixun/article · GitHub
I recommend you a book "Collective smart programming".
All the examples in this section are written in python. You may learn a lot from them by reading all the code.
Compared with python, this
Write in front of the crap:Well, I have to say Fish C markdown Text editor is very good, full-featured. Again thanks to the little turtle Brother's python video Let me last year in the next semester of the introduction of programming, fell in love with the programming of the language, because it is biased statistics, after the internship decided to put the direction of data mining, more and more found the importance of specialized courses. In the days when everyone was busy attending various tra
This paper mainly records the cost function of neural network, the usage of gradient descent in neural network, the reverse propagation, the gradient test, the stochastic initialization and other theories, and attaches the MATLAB code and comments of the relevant parts of the course work.
Concepts of neural networks, models, and calculation of predictive classification using forward propagation refer to Andrew Ng
This chapter summarizesA brief introduction to machine learning.
The 1th Chapter Introduction Basic Terms Hypothesis spatial inductive preference Development course and application actuality
The 1th Chapter Introduction
The research content of machine learning is the algorit
Label: style blog HTTP Io ar use strong SP data
Machine Learning Courses
Requirements: Basic linear algebra (matrix, vector, matrix vector multiplication), basic probability (probability of random variables and basic attributes), and Calculus
Machine Learning: Course
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