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adopted before we really saw hope.Back to the example of identifying stop signs, if we train the network, train the network with a lot of wrong answers, and adjust the network, the results will be better. What the researchers need to do is train them to collect tens of thousands of or even millions of pictures, until the weight of the artificial neuron input is highly accurate, so that every judgment is correct-whether it's fog or fog, it's sunny or rainy. At this point the neural network can "

Original handout of Stanford Machine Learning Course This resource is the original handout of the Stanford machine learning course, which is AndrewNg said that a total of 20 PDF files cover some important models, algorithms, and concepts in machine learning. This compress will be uploaded and shared with you. You can click on the right side to download the original lecture. Zip. Stanford Machine Learning Open Course video This is a video of the o

remark is not "too" and.in today's big Data age,Domestic people advocate operating system to "domestic",instead of "intelligent',This is not a history of reversing?Intelligent systems are not secure?It's totally sunstroke ..Is it safer to stay in a subsystem ??recently,Baidu Li puts forward proposals for building a nationwide intelligent open platform,will bring insecurity to the country .?strictly speaking,,unmanned automatic intelligent Machine(like,self-driving airplanes and cars)safer than

function: g (z) =1/(1+e-z), also represented as hθ (X) =g (ΘTX).In order to implement the logistic regression classifier, we need to take a regression coefficient in each feature and then add all the result values , substituting the sum into the sigmoid function to get the value between 0~1.You can now categorize the label Y:where θtx=0 namely θ0+θ1*x1+θ2*x2=0 is called the decision boundary namely Boundarydecision.Cost function:The cost function of linear regression is based on the least squar

this rule, we can design the corresponding algorithm so that J takes the minimum value.Method One: Batch gradient descentThe meaning is simple, each iteration iterates over all m-known samples until it converges.Repeat until convergence{(For every J)}Method Two: Random gradient descentThere is a big problem with the batch gradient drop, and when the number of data sets is very large, it takes a long time to iterate. Using random gradient descent although it is possible to take some "detours", b

This blog aims to discuss the learning rate of linear regression gradient decline, which andrewng in the public class, and discusses the problem of gradient descent initial value with an example.The learning rate in linear regression gradient descentIn the previous blog, we deduced the linear regression and used the gradient descent to solve the parameters in the linear regression. But we did not take into account the problem of learning rate.We still