list of machine learning models

and visualize data. Through various examples, the reader can learn the core algorithm of machine learning, and can apply it to some strategic tasks, such as classification, prediction, recommendation. In addition, they can be used to implement some of the more advanced features, such as summarization and simplification.I've seen a part of this book before, but the internship involves working with the data

Tai Lin Xuan Tian • Machine learning CornerstoneYesterday began to see heights field of machine learning Cornerstone, starting from today refineFirst of all, the comparison of the basis, some of the concepts themselves have already understood, so no longer take notes, a bit of the impression is about the ML, DL, ai som

sentence The main task of pattern recognition is to design a classifier that is invariant to these transformations, with the following three techniques: Structural invariance: The design of the structure has taken into account the insensitivity to the transformation, and the disadvantage is that the number of network connections becomes large Training invariance: Different sample training parameters for the same target; disadvantage: It is not guaranteed that the tr

using K-fold cross-validationA key step in building a machine learning model is to evaluate the performance of the model on new data.Common cross-validation techniques: holdout cross-validation and K-fold cross-validation.Holdout cross-validationHoldout cross-validation is a classic and common method for evaluating the generalization performance of machine

Today we share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-exercise solution for job three. I encountered a lot of difficulties in doing these topics, when I find the answer on the Internet but can not find, and Lin teacher does not provide answers, so I would like to do their own questions on how to think about the writing down,

tree model. The decision tree learning algorithms include ID3 and C4.5. From: http://www.cnblogs.com/LeftNotEasy/archive/2011/01/02/machine-learning-boosting-and-gradient-boosting.html At the end of the previous chapter, I mentioned that I have already written almost all the articles about linear classification. However, I suddenly heard that the Team has rece

The problem of selecting the Training sample sizeThe accuracy of model learning is related to the size of the data sample, so how do you show the relationship between more samples and better accuracy?We can continue to increase the training data until the model accuracy stabilizes. This process is a great way to understand how sensitive your system is to sample sizes and adjustments.Therefore, the training sample must first not be too little, too litt

online: when the user submits the obvious signs, the user's model is updated immediately. The original data streams generated when the user interacts with the application must be saved. In this way, you can re-run the raw stream data required for machine learning for user interest later, and avoid errors during the process of uploading the data due to the fragile cache, as a result, the data is lost. The

-party library, without organic integration, the corresponding learning costs will be higher. Python is faster than R. Python can directly deal with the data on the G, R No, r analysis data need to first through the database to transform big data into small data (through GroupBy) to the R for analysis, so R can not directly analyze the behavior of the list, can only analyze statistical results. Python's adv

what parameter settings are stable on different datasets.I recommend that you start with a medium complexity algorithm. Choose one that has been fully understood, there are many optional open source implementations, and you need to explore an algorithm with a small number of parameters. Your goal is to build intuition about how the algorithm behaves in different problems and settings.Use a machine learning

train our models. Let's see what methods are available and what parameters are required as input. First we import the built-in library file als:import org.apache.spark.mllib.recommendation.ALSThe next operation is done in Spark-shell. Under Console, enter ALS. (Note that there is a point behind the ALS) plus the TAP key:The method we are going to use is the train method.If we enter Als.train, we will return an error, but we can look at the details of