aws lambda machine learning

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

by-xx:metaspacesize and-xx:maxmetaspacesize respectively.Reference Correct posture using the Java8 Optional Java 8 Optional class depth parsing Java8 lambda expression 10 examples Streams API in Java 8 The ultimate guide to new features in Java 8 Tips: This article belongs to their own study and practice of the process of recording, many pictures and text are pasted from the online article, no reference please forgive! I

minimum point, is the lambda we need to select.Learning CurvesThe error and training set size are used as function images as learning cruvers.The following is the case where the algorithm is in high deviation (underfit).The judgment model is in high Bias:Sample less: Jtrain low, JCV high;More samples: Jtrain, JCV are high, and Jtrain ~JCVIf the algorithm is in high bias, adding more training samples will n

Source: https://www.cnblogs.com/jianxinzhou/p/4083921.html1. The problem of overfitting (1) Let's look at the example of predicting house price. We will first perform linear regression on the data, that is, the first graph on the left. If we do this, we can obtain such a straight line that fits the data, but in fact this is not a good model. Let's look at the data. Obviously, as the area of the house increases, the changes in the housing price tend to be stable, or the more you move to the right

. -Get more training samples -Try to use a set with fewer features -Try to obtain other features -Try to add multiple combinations of features -Try to reduce λ -Add Lambda Machine Learning (algorithm) diagnosis (Diagnostic) is a testing method that enables you to have a deep understanding of a Learning Algorithm and kn

-virginica 6.588 2.974 5.552 2.026Df.groupby (' class '). describe ()Data is split by class and descriptive statistics are given separatelyPetal length \Count mean std min 25% 50% 75% maxClassIris-setosa 50.0 1.464 0.173511 1.0 1.4 1.50 1.575 1.9Iris-versicolor 50.0 4.260 0.469911 3.0 4.0 4.35 4.600 5.1Iris-virginica 50.0 5.552 0.551895 4.5 5.1 5.55 5.875 6.9Petal width ... sepal length sepal width \Count mean ... 75% max Count meanClass ...Iris-setosa 50.0 0.244 ... 5.2 5.8 50.0 3.418Iris-versi

-virginica 6.588 2.974 5.552 2.026Df.groupby (' class '). Describe ()Data is split by class and descriptive statistics are given separatelyPetal length \Count mean std min 25% 50% 75% maxClassIris-setosa 50.0 1.464 0.173511 1.0 1.4 1.50 1.575 1.9Iris-versicolor 50.0 4.260 0.469911 3.0 4.0 4.35 4.600 5.1Iris-virginica 50.0 5.552 0.551895 4.5 5.1 5.55 5.875 6.9Petal width ... sepal length sepal width \Count mean ... 75% max Count meanClass ...Iris-setosa 50.0 0.244 ... 5.2 5.8 50.0 3.418Iris-versi

give more training data. Cross-validation set errors or test set errors do not degrade much. Therefore, it is significant to be able to see that the algorithm is in a high-variance situation, because it avoids wasting time collecting more training set data. Because no number of data is meaningless.Let's take a look at what the learning curve should look like when the learning algorithm is at a high varianc

WEEK1:Machine learning: A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves with experience E. Supervised learning:we already know what we correct output should look like. Regression:try to map input variables to some continuous function.

[Machine learning algorithm-python implementation] matrix denoising and normalization, python Machine Learning1. The background project is required. We plan to use python to implement matrix denoising and normalization. The numpy mathematical library does not find ideal functions. Therefore, I wrote a de-noise and normalization algorithm in the standard library,

Application Recommendations for machine learningFor a long time, the machine learning notes have not been updated, the last part of the updated neural network. This time we'll talk about the application of machine learning recommendations.Decide what to do nextSuppose we nee

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. deciding what to try next ( Determine what to do next ) I have already introduced some machine learning methods. It is obviously not enough to know the specific process of these methods. The key is to learn how to use them. The so-called best way to master knowledge is to put it into practice. Consider the ear

Customer Churn "Loss rate" is a business term that describes the customer's departure or stop payment of a product or service rate. This is a key figure in many organizations, as it is usually more expensive to get new customers than to retain the existing costs (in some cases, 5 to 20 times times the cost). Therefore, it is invaluable to understand that it is valuable to maintain customer engagement because it is a reasonable basis for developing retention policies and implementing operational

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

different lambda, the calculated training error and cross-validation error are as follows:Lambda Train error Validation error 0.000000 0.173616 22.066602 0.001000 0.156653 18.597638 0.003000 0.190298 19.981503 0.010000 0.221975 16.969087 0.030000 0.281852 12.829003 0.100000 0.459318 7.587013 0.300000 0.921760 1.000000 2.076188 4.260625 3.000000 4.901351 3.822907 10.000000 16.092213 9.94

new feature $f$ given the $x$ of a data point. When $\THETA^TF \geq 0$, predict $y=1$, and conversely, predict $y=0$.Training (Training): $$\min\limits_\theta c\left[\sum\limits_{i=1}^{m}y^{(i)}cost_1 (\theta^tf^{(i)}) + (1-y^{(i)}) Cost_0 ( \theta^tf^{(i)}) \right] + \frac{1}{2}\sum\limits_{j=1}^{n}\theta_{j}^2$$Effect of parameter C ($\approx\frac{1}{\lambda}$): Large c:low bias, high variance Small c:high bias, low variance Effec

-core processor is a necessity, not a luxury. Tool Python jug, a small Python framework that manages computations that take advantage of multicore or host computers. Cloud service platform, Amazon Web services platform, AWS. 13. More Machine learning Knowledge: Online resources: Andrew Ng

/m;18 j = j + lambda* (sum (SUM (Theta1 (:, 2:end). ^2)) +sum (SUM (THETA2 (:, 2:end). ^2))/2/m;% backward Propagation Delta1 = zeros (Size (Theta1)); % 25x40122 Delta2 = zeros (Size (THETA2));%10x2623 for I=1: M24 delta3 = A3 (i,:) '-Y_vect (i,:) ';%10x125 tempTheta2 = Theta2 ' * DELTA3; % 26x10x10x1 = 26x1-Delta2 = TempTheta2 (2:end). * Sigmoidgradient (Z2 (i,:) ');%25x1 Delta2 = Delta2 + delta3 * A2 (I,: );