data scientist vs machine learning engineer

Spark Machine Learning Mllib Series 1 (for Python)--data type, vector, distributed matrix, API Key words: Local vector,labeled point,local matrix,distributed Matrix,rowmatrix,indexedrowmatrix,coordinatematrix, Blockmatrix.Mllib supports local vectors and matrices stored on single computers, and of course supports distributed matrices stored as RDD. An example of

In the process of learning machine learning algorithms, we often need data to validate algorithms and debug parameters. But it's not that easy to find a set of data samples that are perfectly suited to a particular type of algorithm. Fortunately NumPy, Scikit-learn all provi

To facilitate the understanding of the later learning, record!Run-time Data area1. Thread Sharing1.1 Method Area1.1.1 Running a constant-volume pool (runtime Constant)1.2 Heaps (heap)2. Thread-Private2.1 Virtual machine stack (VM stack)2.2 Local method Stack (Native)2.3 Procedure Counter (program Counter Register)3. Direct MemoryVirtual

are two issues to note:1, if the data is linearly non-divided.When the data is linearly non-divided, we can also use the above method, but will come to an unacceptable solution, at this time we can detect whether the solution is valid to determine whether our data can be divided.2. What happens if W0 exists in Z?In our previous assumptions, W0 represents a const

("Training"); Trainingdata.foreach (function(item) {classifier.adddocument (Item.text, Item.label); }); varStartTime =NewDate (); Classifier.train (); varEndTime =NewDate (); varTrainingtime = (endtime-starttime)/1000.0; Console.log ("Training Time:", Trainingtime, "seconds"); Loadtestdata ();}functionLoadtestdata () {Console.log ("Loading test Data"); Fs.readfile (' Test_data.json ', ' utf-8 ',function(err,

http://blog.csdn.net/ppn029012/article/details/8908104 Machine Learning---2. From maximum likelihood to view linear regression classification: Mathematics machine Study 2013-05-10 00:34 3672 people read comments (15) Collection Report MLE machine learning Directory (?) [+]

Netfei is a DVD leasing company. by increasing its sales by 10%, it can earn 1 million RMB in revenue, which is very impressive. How to: predict consumers' ratings for movies? (Increase the predicted value by 10 percentage points through their own systems) if the recommendations you provide to consumers are very accurate, the consumers will be very satisfied. The essence of machine learning: 1. An existin

sessions should be conducted before they can be completed?In general, the number of sessions = total size of the sample/out-of-sample data. SizeHow many data should you choose to use as an out-of-sample data?The different requirements have different options, but one rule of thumb is:Out-of-sample data size = Total siz

length of 20. Now the machine has 8 GB of memory. How can this problem be solved. Iii. System Design Questions Forward maximum matching algorithm (FMM) for Chinese Word Segmentation in natural language processing ). Note: The example explains the basic idea of FMM. (1) design the data structure struct dictnote of the dictionary. (2) Use C/C ++ to implement FMM. The optional interface is Int FMM (vector He

#数据预处理方法, mainly dealing with the dimension of data and the problem of the same trend.Import NumPy as NPFrom Sklearn Import preprocessing#零均值规范Data=np.random.rand (3,4) #随机生成3行4列的数据Data_standardized=preprocessing.scale (data) #对数据进行归一化处理, that is, each value minus the mean divided by the variance is primarily used for SVM#线性数据变换最大最小化处理Data_scaler=preprocessing. M

)) +geom_point ()#加平滑模式Ggplot (Heights.weights, aes (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (HEIGHTS.WEIGHTS[1:20,], AES (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (heights.weights[1:200,], AES (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (heights.weights[1:2000,], AES (x = Height, y = Weight)) +geom_point () +geom_smooth ()Ggplot (Heights.weights, aes (x = Height, y = Weight)) +Geom_point (AES (color = Gender, alpha = 0.25)) +Scale_al

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determine the type of input vector x of the calculation process to specify the naïve Bayesian computation processBy the conditional probability formula get P (y=ck| x=x) = P (y=ck,x=x)/P (x=x) = P (x=x | Y=CK) P (y=ck)/P (x=x)The full probability formula is available (replace P (x=x)):　　　　　　　　　　　　　　　　　　　　　　　　　　　Note: Argmax refers to CK with the largest probability of taking　　　One of the I (..) is the indicator function, of course, these probabilities in the actual can be very block, you can se

ImageNet: non-commercial visualisation of big dataAs of May 1, 2015, the Imagenet database has more than 15 million images. cifar10:10 Types of object recognition data setsData set contains 60,000 images of 32*32, total 10 objects (6,000 images/class)Among them, 50,000 as training images,10,000 as testing imagesmnist : handwritten font recognition data set10 types of d