machine learning course on coursera

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

, the minimum value of the price function jval provided by us, of course, returns the solution of the vector θ. The above method is obviously applicable to regular logistic regression.5. Conclusion Through several recent articles, we can easily find that both linear regression and logistic regression can be solved by constructing polynomials. However, you will gradually find that more powerful non-linear classifiers can be used to solve polynomial reg

Machine learning is a comprehensive and applied discipline that can be used to solve problems in various fields such as computer vision/biology/robotics and everyday languages, as a result of research on artificial intelligence, and machine learning is designed to enable computers to have the ability to learn as humans

install Anacona. With Anaconda, you will be able to start using Python to explore the world of machine learning. The default installation library for Anaconda contains the tools needed for machine learning.Basic Machine learning SkillsWith some basic Python programming skil

Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduc

As an article of the College (http://xxwenda.com/article/584), the follow-up preparation is to be tested individually. Of course, there have been many tests. Apache Spark itself1.MLlibAmplabSpark was originally born in the Berkeley Amplab Laboratory and is still a Amplab project, though not in the Apache Spark Foundation, but still has a considerable place in your daily GitHub program.ML BaseThe mllib of the spark itself is at the bottom of the three

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

to the right in this image. We can generally see the two learning curves, the two curves of blue and red are approaching each other. Therefore, if we extend the curve to the right, it seems that the training set error is likely to increase gradually. The cross-validation set error will continue to decline. Of course, we are most concerned with cross-validation set errors or test set errors. So from this pi

Awesome series Awesome Machine Learning Awesome Deep Learning Awesome TensorFlow Awesome TensorFlow implementations Awesome Torch Awesome Computer Vision Awesome Deep Vision Awesome RNN Awesome NLP Awesome AI Awesome Deep Learning Papers Awesome 2vec Deep

1.1 machine learning basics-python deep machine learning, 1.1-python Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang Video tutorial: http://pan.baidu.com/s/1kVNe5EJ 1. course Introduction 2.

design a system that allows it to learn in a certain way based on the training data provided; With the increase of training times, the system can continuously learn and improve the performance, through the learning model of parameter optimization, it can be used to predict the output of related problems. 4. Machine Learning Algorithm Classification: (1) Supervi

for free and integrate right away with our beautiful API.Want to learn more?There is plenty of online resources out there to learn on machine learning! Here is a few: A comprehensive guide for a machine learning project on a Jupyter Notebook, if you want to see what the some code looks like. Our Gentle-to

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

Original address: http://blog.csdn.net/lrs1353281004/article/details/79529818 Sorting out the machine learning-algorithm engineers need to master the basic knowledge of machine learning, and attached to the internet I think that write a better blog address for reference. (Continuous update)

Directory 1. Introduction 1.1. Overview 1.2 Brief History of machine learning 1.3 Machine learning to change the world: a GPU-based machine learning example 1.3.1 Vision recognition based on depth neural network 1.3.2 Alphago 1.3.

be struggling. So the bean leaf emphasizes the importance of a good foundation. Once you have mastered the basics of mathematics, your understanding of these models can easily transcend the formula itself.The difference between deep knowledge and shallow knowledgeBean leaves think that when we learn knowledge, we should learn to differentiate, what is deep knowledge (knowledge), what is shallow knowledge (shallow knowledge).Some knowledge is shallow knowledge, only need to remember to know. But

, as shown in: Step 4: run the model. After completing the preceding operations, you can run the program. Click "run" at the bottom to run the model. After each module is run, a green check box is displayed in the upper right corner, if an error occurs in each module or step, a red icon will appear in the same place. After you move the mouse over it, an error type will be displayed. Step 5: view the result. Right-click the dot in the "Evaluate Model" box and select "Visualize" to view the mode

Recently saw a relatively good machine learning course, roughly heard it again. The overall sense of machine learning field is still more difficult, although Li Hongyi teacher said is very good, not enough to absorb up or have a certain difficulty. Even though the process ha

emerging. The text of the formula looks a bit around, below I send a detailed calculation process diagram.Refer to this: Http://www.myreaders.info/03_Back_Propagation_Network.pdf I did the finishing Here is the calculation of a record, immediately update the weight, after each calculation of a piece is immediately updated weight. In fact, the effect of batch update is better, the method is not to update the weight of the case, the record set of each record is calculated once, the added valu

clearly explained. It also covers De Rham cohomology and Lie algebra, where audience is invited to discover the beauty by linking geometry wi Th algebra.Modern Graph theoryBela BollobasIt is a modern treatment of this classical theory, which emphasizes the connections and other mathematical subjects--fo R example, random walks and electrical networks. I found some messages conveyed by the This book was enlightening for my all in machine