data science stanford coursera

Stanford University machine Learning lesson 10 "Neural Networks: Learning" study notes. This course consists of seven parts: 1) Deciding what to try next (decide what to do next) 2) Evaluating a hypothesis (Evaluation hypothesis) 3) Model selection and training/validation/test sets (Model selection and training/verification/test Set) 4) Diagnosing bias vs. variance (diagnostic deviation and variance) 5) Regularization and bias/variance (Regularization

and does not start to talk about it. Instead, it goes straight to the next question, and the regular expression should be free to learn. This avoids the problem of having no idea which corner to go around as python learns more and more. The main teaching thread of this course is very clear! I only need to use python for data analysis. Can I put aside other powerful python functions first? The answer to this course is yes. 4. The course has been compl

not know which corner to go around. The main line of this course is very, very clear! I just need to use Python to analyze the data. What about Python's power to put aside, please? The answer to this course is, yes. 4, the course has been completed, more than x cool PO main hair video sent one or two episodes and then do not know where to go to make people more comfortable. Please follow the rhythm step by step "complete" the Python "

An Introduction to the Data Science series at the University of johnkins In the past few months, I have taken Andrew Ng from Stanford University as a reference for his machine learning handout, on the CSDN blog, I wrote some summary notes related to machine learning and data mining (separate component analysis and rein

manual optimization in the homework similarity matrix, we need to calculate the similarity of 22 documents, which is actually a matrix operation. 1) The code is as follows, spents 1m22.042sSelect X.docid,y.docid,sum (X.count*y.count) as Count from Frequency X, Frequency y where x.term = Y.term and X.docid 2) Submit the answer only need, one of the results, time 1m10.919s, you can see here is actually calculated the similarity of all documents intercepted, DB is not optimized.SELECT * FROM (sele