machine learning with tensorflow book

recently in the "Machine learning actual Combat" this book, because I really want to learn more about machine learning algorithms, coupled with want to learn python, in the recommendation of a friend chose this book to learn. A. A

the author is two-dimensional [x1, x2], while the program adds one-dimensional [X0 = 1, x1, x2]. the strange thing is that x0 is added to the first position, not the last position. In addition, the author of the formula at the Red Line in the Chinese painting did not give its origin. After searching online, he found a blog post and wrote it well. Here is a brief overview of the post: The specific process is as follows: (reference: http://blog.csdn.net/yangliuy/article/details/18504921? Reload)

, but not more than the parents (saying this sentence in my side of the perfect embodiment, so that I distress why the shortest in the family). This phenomenon is the child's height toward the average height back (return). Although the relationship between the numerical prediction and the fallback phenomenon is not very big, but the people are Darwin's cousin, so they quoted the designated academic name of the family ~In the last section, we talked about the problem of the monthly price forecast

is to test a series of learned g and find the g that minimizes the Eout as the final output.The two methods will be explained in the next two sections. The final result obtained by the first method is as follows:Course Summary:In the past, when studying data mining courses, I also heard that we should not over-fitting, but the book does not seem to explain why over-fitting is not good (as the teacher did not say ), I didn't understand it at the t

0.0267 0 11 0.245 0.057 0 12 0.343 0.099 0 13 0.639 0.161 0 14 0.657 0.198 0 15 0.36 0.37 0 16 0.593 0.042 0 17 0.719 0.103 0 Logistic regression:refer to the "machine learning combat" co

Machine Learning tutorial Http://robotics.stanford.edu/people/nilsson/mlbook.html Reinforcement Learning: An Introduction Http://www-anw.cs.umass.edu /~ Rich/book/the-book.html The Journal of machine learning research Http://ww

want to go down the hill, how can you go down the hill as soon as possible (by default, the speed is constant and you will not die )? You should look around and find the steep current direction to go down the hill? In this direction, the gradient can be used for calculation, which is the source of the gradient descent method. Do you think it is very simple, think you have mastered it? Haha, it's still too young. I will not go into details about this part. I will provide two materials for my stu

greater than a certain constant), this may reduce the number of cases where the data is separated, which makes the VC dimension decrease, which makes the model complexity effectively controlled.NextFrom the above, the VC interpretation can be a simple conclusion, that is, SVM limit can effectively reduce the VC dimension, control complexity, get better generalization ability, and if we can combine the nonlinear transformation, we can get the complex boundary. Next, it will extend to the linear

. Regular items are generally used at the time of overfitting, simply speaking is the limit of the weight of the size, L2 is the sum of the weights of the square and added to the target function, and finally the weight tends to smooth, there is a regular term is L1, can make weights tend to sparse, that is, many of the 0. Change the network structure is generally a relatively fine adjustment, such as activation from Relu to Prelu, a layer of convolution core size changes, and so on, general smal

the structure of the book: From a macro point of view, the entire Java technology system, Java and JVM development process, modularity, and JDK compilation Explains the automatic memory management of the JVM, including the partitioning principle of the memory area of the virtual machine and the causes of various memory overflow anomalies. The execution subsystem of virtual

data points, which involves the mapping of non-linear data to high-dimensional to achieve the purpose of linear divisible data.Support Vector Concepts: The above sample map is a special two-dimensional situation, of course, the real situation may be many dimensions. Start with a simple understanding of what a support vector is at a low latitude. Can see 3 lines, the middle of the red line to the other two first distance is equal. The red one is the hyper-plane that SVM looks for in two-di

cheek, and has a black-box glasses. There are just a few of these characteristics, let others have a clear understanding in their minds. In fact, there are countless characteristics on the human face. The reason why we can describe this is that, because human beings have a very good ability to extract important features and let machines learn to extract important features, SVD is an important method. In the field of machine

clustering are generally relatively random, generally not very ideal, and the final result tends to be indistinguishable from natural clusters, in order to avoid this problem, the binary K mean clustering algorithm is used in this paper .The implementation of the binary K-means clustering Python is given in the next blog post.Complete code and test data can be obtained here, or you want to get the source from the connection, because the copy code from the page will appear without indentation, y

model: Typically, you learn a model that represents the target, and then use it to search the image area and then minimize the refactoring error. Similar to the build model describes a goal, then the pattern matching, in the image to find the best match with the model of the region, is the target.Discriminant Model: The tracking problem is considered as a two classification problem, and then the decision boundary of the target and the background is found. It doesn't matter how the goal is descr

), though it's no better than Microsoft's Visual Studio, but it's much more than the one that comes with it-if it's written in C, Helpless is written in Java, startup speed huge slow ~ ~Recently turned over the book "Machine Learning in Action". The book uses Python to implement some

Reprint please indicate the Source: Bin column, Http://blog.csdn.net/xbinworldThis is the essence of the whole fifth chapter, will focus on the training method of neural networks-reverse propagation algorithm (BACKPROPAGATION,BP), the algorithm proposed to now nearly 30 years time has not changed, is extremely classic. It is also one of the cornerstones of deep learning. Still the same, the following basic reading notes (sentence translation + their o

This is the essence of the whole fifth chapter, will focus on the training method of neural networks-reverse propagation algorithm (BACKPROPAGATION,BP), the algorithm proposed to now nearly 30 years time has not changed, is extremely classic. It is also one of the cornerstones of deep learning. Still the same, the following basic reading notes (sentence translation + their own understanding), the contents of the b