edx machine learning course

, linear algebra library to accelerate the calculation, the smaller batch, the acceleration effect may be less obvious. Of course, batch is not the bigger the better, too big, the weight of the update will be less frequent, resulting in the optimization process is too long. So mini-batch size, not static, according to your data set size, your device computing ability to choose. The the-Go is therefore-use some acceptable (but not necessarily-

search first search the current directory, and then according to the order of the directory configuration to find, and then run, so the Classpath directory The configuration is in sequence(2) Path environment variable (master)(1) The role of the PATH environment variable guarantees that the Javac command can be run in any directory. The same can be configured QQ and other (2) path configuration of two scenarios: A: Scenario 1 (understanding) B: Scenario 2 Find the location of the environment va

Draw a map, there is the wrong place to welcome correct:In machine learning, features are critical. These include the extraction of features and the selection of features. They are two ways of descending dimension, but they are different:feature extraction (Feature Extraction): creatting A subset of new features by combinations of the exsiting features. In other words, after the feature extraction A feature

problem solution.Or simply, it can be understood that finding a reasonable hyper-plane in a high-dimensional space separates the data points, which involves the mapping of non-linear data to high-dimensional to achieve the purpose of linear divisible data.　　　　　　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 latitu

(written in front) said yesterday to write a machine learning book, then write one today. This book is mainly used for beginners, very basic, suitable for sophomore, junior to see the children, of course, if you are a senior or a senior senior not seen machine learning is al

methods use optimization algorithms directly or indirectly.According to the function and form similarity of the algorithm, we can classify the algorithm, for example, tree-based algorithm, neural network based algorithm and so on. Of course, the scope of machine learning is very large, and some algorithms are difficult to classify into a certain category. For so

Reprint Please specify source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectory machine Learning Cornerstone Note When you can use machine learning (1) Machine learning Cornerstone Note 2--When you can use

of the network is changed appropriately, so that the training process can converge faster and more stably.2.1.1 Increase momentum termFeedforward Network in the course of training, loss function often concussion, resulting in the training process is not convergent, in order to reduce the impact of this problem, you can try to use the smooth loss function of the oscillation curve to speed up the training process, through the design of low-pass filter

This article is a translation of the article, but I did not translate the word by word, but some limitations, and added some of their own additions.Machine Learning (machines learning, ML) is what, as a mler, is often difficult to explain to everyone what is ML. Over time, it is found to understand or explain what machine lea