tensorflow for deep learning from linear regression to reinforcement learning

functions for each gradient descent. Let's draw a value like this:# 画出每一次迭代和损失函数变化theta , Cost_J = gradientDescent(X, y)print(‘theta: ‘,theta.ravel())plt.plot(Cost_J)plt.ylabel(‘Cost J‘)plt.xlabel(‘Iterations‘);The results obtained are theta to[-3.63029144, 1.16636235]So our equation is h=-3.63029144+1.16636235x , we draw a line like this,plt.scatter(X[:,1],y,s=30,c=‘r‘,marker=‘x‘,linewidths=1)plt.xlim(4,24)plt.xlabel(‘Population of City in 10,000s‘)plt.ylabel(‘Profit in $10,000s‘)xx = np.arang

Deep Learning (depth learning) Learning notes finishing Series[Email protected]Http://blog.csdn.net/zouxy09ZouxyVersion 1.0 2013-04-08Statement:1) The Deep Learning Learning Series is a

Deep Learning (depth learning) Learning notes finishing Series[Email protected]Http://blog.csdn.net/zouxy09ZouxyVersion 1.0 2013-04-08Statement:1) The Deep Learning Learning Series is a

Closure of Python deep learning and deep learning of python Closure is an important syntax structure for functional programming. Functional programming is a programming paradigm (both process-oriented and object-oriented programming are programming paradigms ). In process-oriented programming, we have seen functions; i

6-support Vector RegressionFor the regression with squared error, we discuss the kernel ridge regression.With the knowledge of kernel function, could we find a analytic solution for kernel ridge regression?Since we want to find the BestβnHowever, compare to the linear situation, the large number of data would suffer from this formation ofβn.Compared to Soft-margi

Original: http://blog.jobbole.com/87148/Editor's note "for an old question on Quora: What are the advantages of different classification algorithms?" Xavier Amatriain, a Netflix engineering director, recently gave a new answer, and in turn recommended the logic regression, SVM, decision tree integration and deep learning based on the principles of the Ames Razor,

"Editor's note" for an old question on Quora: What are the advantages of different classification algorithms? Xavier Amatriain, a Netflix engineering director, recently gave a new answer, and in turn recommended the logic regression, SVM, decision tree integration and deep learning based on the principles of the Ames Razor, and talked about his different understa

Preface: Recently, I intend to learn some theoretical knowledge of deep learing in a slightly systematic way, and intend to use Andrew Ng's Web tutorial Ufldl Tutorial, which is said to be easy to read and not too long. But before this, or review the basic knowledge of machine learning, see Web page: http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=DeepLearning. The content is actually ver

Connect Because we want to learn the expression of features, we need to know more about features or hierarchical features. So before we talk about deep learning, we need to explain the features again (haha, we actually see such a good explanation of the features, but it is a pity that we don't put them here, so we are stuck here ). Iv. Features Features are the raw material of the machine

industry for image classification with KNN,SVM,BP neural networks. Gain deep learning experience. Explore Google's machine learning framework TensorFlow. Below is the detailed implementation details. System Design In this project, 5 algorithms for experiments are KNN, SVM, BP Neural Network, CNN and Migration

Learning Google's deep learning finally a little bit of the prospect, to share my tensorflow learning process. TensorFlow's official Chinese document is jerky, and the dataset has been used in the Mnist binary dataset. And not much about how to build their own picture datase

datasets and tasks, such as object detection, tracking, retrieval, and so on. Another famous competition in the field of computer vision is psacal VOC. However, it has a small training set and is not suitable for training deep learning models. Some scholars will learn the characteristics of imagenet on the PSACALVOC on the object detection, the detection rate increased 20%[10].Since feature

, and so is the Logreg.So this can be promoted.In fact, it can be, for L2 regularization this form of linear model is possible, as follows.The question above is: in exactly the linear model that conforms to L2, W can certainly be expressed as a linear combination of Zn.The more intuitive proof here is that the core is to split w into components parallel to the Z

Objective The two models in this chapter are logarithmic linear models. Logistic distribution If the variable x obeys the logistic distribution, then the distribution of x must be symmetric on the Y axis. The curve grows faster in the center part. Both ends grow slowly. Two Logistic regression models The essence is the conditional probability P (y| X). It also means that given x, the m

Tags: Environment configuration EPO Directory decompression profile logs Ros Nvidia initializationThis article is a personal summary of the Keras deep Learning framework configuration, the shortcomings please point out, thank you! 1. First, we need to install the Ubuntu operating system (under Windows) , which uses the Ubuntu16.04 version: 2. After installing the Ubuntu16.04, the system needs to be initial

Introduction of Style migration Style Transfer is one of the most interesting applications of deep learning, as shown in this way, we can use this method to "migrate" the style of a picture to another picture: However, the speed of the original style migration (click to view the paper) is very slow. On the GPU, it takes about 10 minutes to generate a picture, and it may take several hours if you use only t

?Because we know the advantage of SVM, which is able to simplify the computing by kernel, while the Logreg holds some other Benefits.Here we apply the Platt ' s Scaling https://en.wikipedia.org/wiki/Platt_scalingWhich is found to being a nice method to better the binary problem.We caculate the transforming of the SVM to get the W and B, and we all tool to find best A and B.In conclusion, the structure of our demand are like that:We want to use KERNEL, we need wt*z (to package into KERNEL), we ne

Main Content: Spotify is a music website similar to cool music. It provides personalized music recommendations and music consumption. The author uses deep learning combined with collaborative filtering for music recommendation. Details: 1. Collaborative Filtering Basic principle: two users listen to similar songs, indicating that the two users are interested and have similar tastes. A group of two songs are

Transferred from: http://blog.csdn.net/zouxy09/article/details/8775488 Because we want to learn the characteristics of the expression, then about the characteristics, or about this level of characteristics, we need to understand more in-depth point. So before we say deep learning, we need to re-talk about the characteristics (hehe, actually see so good interpretation of the characteristics, not put here a l

Deep learning reflection with the improvement of computer hardware performance, in-depth learning in today's era as the darling, Computer vision,data mining,nature Language Process .... All take the deep learning of the car, and finally sat on the Boeing airliner. One after