machine learning techniques and algorithms

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-layer ML base, MLI is in the middle layer, a

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. Machine

Bayesian Introduction Bayesian learning Method characteristic Bayes rule maximum hypothesis example basic probability formula table Machine learning learning speed is not fast enough, but hope to learn more down-to-earth. After all, although it is it but more biased in mathematics, so to learn the rigorous and thoroug

continuously updating theta. Map Reduce and Data Parallelism: Many learning algorithms can be expressed as computing sums of functions over the training set. We can divide up batch gradient descent and dispatch the cost function for a subset of the data to many different machines So, we can train our algorithm in parallel. Week 11:Photo OCR: Pipeline: Tex

Machine Learning Summary (1), machine learning SummaryIntelligence:The word "intelligence" can be defined in many ways. Here we define it as being able to make the right decision based on certain situations. Knowledge is required to make a good decision, and this knowledge must be operable, for example, interpreting se

7 machine learning System Design Content 7 Machine Learning System Design 7.1 Prioritizing 7.2 Error Analysis 7.3 Error Metrics for skewed classed 7.3.1 Precision/recall 7.3.2 Trading off precision and RECALL:F1 score 7.4 Data for machine

that employs a scripting language similar to Lisp. In this library, all the statistics-related features you want are available in the R language, including some complex icons. The code in the Machine learning directory in CRAN (which you can think of as a third-party package from a machine brother) is written by a leading figure in the statistical technology app

deduce it into a form that can be directed. (to say the last, I personally think not to remove | | w| |, is also the same can get the final correct classification of the super-plane, is directly using the distance as a loss function is also possible, may be the gradient is more complex, or the perception machine itself is to use the wrong classification points to distinguish, it is useless this loss function.