machine learning podcast

http://sourceforge.net/projects/numpy/files/download the corresponding version of the NumPy, everywhere, find a not python2.7Use Pip, please.Pip Install NumPyDownload finished, the hint does not install C + +, meaning is also to install VS2008, but installed is VS2012, had to download a VC for Pythonhttp://www.microsoft.com/en-us/download/confirmation.aspx?id=44266Re-pip, wait for the most of the day, the final count is successfulInput command introduced NumPyFrom numpy Import *Operation:InputRa

1. Common steps 2. Chinese participle 1 This is relative to the English text affective analysis, Chinese unique preprocessing. 2 Common methods: Based on the dictionary, rule-based, Statistical, based on the word annotation, based on artificial intelligence. 3 Common tools: Hit-language cloud, Northeastern University Niutrans statistical Machine translation system, the Chinese Academy of Sciences Zhang Huaping Dr. Ictclas, Posen technology, stutterin

intervention on the results of model training it's a lever. Model does not understand the business, really understand the business is people. What the model can do is to learn from the cost function and sample, and find the optimal fit of the current sample. Therefore, machine learning workers should be appropriate to the needs of the characteristics of some human intervention and "guidance", such as the h

p.s. SVM is more complex, the code is not studied clearly, further learning other knowledge after the supplement. The following is only the core of the knowledge, from the "machine learning Combat" learning summary. Advantages:The generalization error rate is low, the calculation cost is small, the result is easy to ex

1. Vector Norm Norm, Norm, is a concept similar to "Length" in mathematics, which is actually a kind of function.The regularization (regularization) and sparse coding (Sparse coding) in machine learning are very interesting applications.For Vector a∈rn A\in r^n, its LP norm is | | a| | p= (∑IN|AI|P) 1p (1) | | a| | _p= (\sum_i^n |a_i|^p) ^{\frac 1 p} \tag 1Commonly used are: L0 NormThe number of elements i

Cross entropy cost function 1. Cross-entropy theory Cross entropy is relative to entropy, as covariance and variance. Entropy examines the expectation of a single information (distribution): H (p) =−∑I=1NP (xi) Logp (xi) Cross-Entropy examines the expectations of two of information (distributions):H (P,Q) =−∑I=1NP (xi) logq (xi)For details, please see Wiki Cross entropy y = Tf.placeholder (Dtype=tf.float32, Shape=[none, ten]) ... Scores = Tf.matmul (H, W) + b probs = Tf.nn.softmax (scores) l

The concept of extreme learning machineElm is a new fast learning algorithm, for TOW layer neural network, elm can randomly initialize input weights and biases and get corresponding output weights.For a single-hidden-layer neural network, suppose there are n arbitrary samples, where。 For a single hidden layer neural network with a hidden layer node, it can be expressed asWhere, for the activation function,

In the introduction of recommendation system, we give the general framework of recommendation system. Obviously, the recommendation method is the most core and key part of the whole recommendation system, which determines the performance of the recommended system to a large extent. At present, the main recommended methods include: Based on content recommendation, collaborative filtering recommendation, recommendation based on association rules, based on utility recommendation, based on knowledge

Earlier, we mentioned supervised learning, which corresponds to non-supervised learning in machine learning. The problem with unsupervised learning is that in untagged data, you try to find a hidden structure. Because the examples provided to learners arenot marked, so there

Original address: http://www.cnblogs.com/cyruszhu/p/5496913.htmlDo not use for commercial use without permission! For related requests, please contact the author: [Email protected]Reproduced please attach the original link, thank you.1 BasicsL Andrew NG's machine learning video.Connection: homepage, material.L 2.2008-year Andrew Ng CS229 machine LearningOf course

similar to LWLR, the formula is described in "machine learning combat". The formula adds a coefficient that we set ourselves, and we take 30 different values to see the change of W.STEP5:Ridge return:#岭回归def ridgeregression (data, L): Xmat = Mat (data) Ymat = Mat (l). T Ymean = mean (Ymat, 0) Ymat = Ymat-ymean Xmean = mean (Xmat, 0) v = var (xmat) Xmat = (Xmat-xmean) /V #取30次不同lam岭回

) for in H: Print(i) for in H.flat: print(i)iterating over a multidimensional array is the first axis :if to perform operations on the elements in each array, we can use the flat property, which is an iterator to the array element :Np.flatten () returns an array that is collapsed into one dimension. However, the function can only be applied to the NumPy object, that is , an array or mat, the normal List of lists is not possible. A = Np.array ([[Up], [3, 4], [5, 6]])print(A.flatten

-centralsonatype-oss-snapshots3.1 Production messagesObjectStreamingproducer {DefMain (args:array[String]) {Val random =NewRandom ()Maximum number of events per secondValMaxevents =6Read the list of possible namesVal Namesresource =This.getClass.getResourceAsStream ("/names.csv")Val names = Scala.io.Source.frominputstream (Namesresource). Getlines (). ToList. Head Split (","). ToseqGenerate a sequence of possible productsVal products =Seq ("IPhone Cover"9.99,"Headphones"5.49,"Samsung Galaxy Cove

of the current node is the middle half of the distance of all its leaf nodes is float (NUMLEAFS)/2.0/plottree.totalw* 1, but since the start Plottree.xoff assignment is not starting from 0, but the left half of the table, so also need to add half the table distance is 1/2/plottree.totalw*1, then add up is (1.0 + float (numleafs))/2.0/ Plottree.totalw*1, so the offset is determined, then the X position becomes Plottree.xoff + (1.0 + float (numleafs))/2.0/PLOTTREE.TOTALW3, for Plottree function p

Original address Mathematics is the foundation of computer technology, linear algebra is the basis of machine learning and deep learning, the best way to understand the knowledge of the data I think is to understand the concept, mathematics is not only used for exams in school, but also the essential basic knowledge of the work, in fact, there are many interestin

This is already the third algorithm of machine learning. Speaking of the simple Bayes, perhaps everyone is not very clear what. But if you have studied probability theory and mathematical statistics, you may have some idea of Bayesian theorem, but you can't remember where it is. Yes, so important a theorem, in probability theory and mathematical statistics, only a very small space to introduce it. This is n

Machine learning is accelerating the pace of progress, it is time to explore this issue. Ai can really protect our systems in the future against cyber attacks. Today, an increasing number of cyber attackers are launching cyber attacks through automated technology, while the attacking enterprise or organization is still using manpower to summarize internal security findings, and then compare them with exter

Vi. more hyper-parameters in grid search and K-nearest algorithmVii. Normalization of data Feature ScalingSolution: Map all data to the same scaleViii. the Scaler in Scikit-learnpreprocessing.pyImportNumPy as NPclassStandardscaler:def __init__(self): Self.mean_=None Self.scale_=NonedefFit (self, X):"""get the mean and variance of the data based on the training data set X""" assertX.ndim = = 2,"The dimension of X must be 2"Self.mean_= Np.array ([Np.mean (X[:,i]) forIinchRange (x.shape[1]))

1. The complete course of statistics all of statistics Carnegie Kimelon Wosseman 2. Fourth edition, "Probability Theory and Mathematical Statistics" Morris. Heidegger, Morris H.degroot, and Mark. Schevish (Mark j.shervish) 3. Introduction to Linear algebra, Gilbert. Strong--Online video tutorials are classic 4. "Numerical linear algebra", Tracy Füssen. Lloyd and David. Bao Textbooks suitable for undergraduates 5. Predictive data analysis of machine