Gnu octave ocr
I personally would have voted for python. Octave/Matlab is the only language in which the assignments are accepted. But if you can get through those early units, you will definitely find yourself dealing with new material (and occasionally appreciating the initial slow pace). Ng deals with linear regression, to be rather slow. Of data analysis might find the pace of the class in the early units, where Prof. Students who already have a background in linear algebra or the basics The programming assignments can be a time sink, but I don't think they could have been any shorter and still give valuable practice in using the techniques outlined in the lectures. The exams are fair (although I think some more work could have been done to make many of the questions less ambiguous). Anyone taking this class and completing the assignments will be ready to apply machine learning to at least some simpler real world problems and should be in a position to quickly pick up more advanced techniques for more complex problems. Ng gives very clear expositions of the fundamentals of machine learning. But as far as online learning goes, this course is probably about as good as it ever gets. There are certain aspects of classroom learning that online learning can't give.
Gnu octave ocr how to#
The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. (iii) Best practices in machine learning (bias/variance theory innovation process in machine learning and AI). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. Many researchers also think it is the best way to make progress towards human-level AI.
Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.
In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. direction of x-axis)Ĭv::Mat result = cv::Mat::zeros(1,rowBlock*colBlock,CV_32FC1) Ĭv::Mat result2 = cv::Mat::zeros(x*y,rowBlock*colBlock,CV_32FC1) įor(int i = 0 i(rowIdx,colIdx) = input.Machine learning is the science of getting computers to act without being explicitly programmed. here, your naming of x and y is counter intuitive for me, since I see x being linked to cols normally (e.g. I added it to your code, to test the equality (it would be better to write a function for each one and encapsulate, though ) ) int main() you know the size of the result in the beginning, so allocate it all at onceĬv::Mat result2 = cv::Mat::zeros(xB*yB,rowBlock*colBlock,CV_32FC1) įor(int i = 0 i(rowIdx,colIdx) = input.at(i+yy, j+xx) This one is much faster for me: int main()Ĭv::Mat input = cv::Mat::eye(100,100,CV_32FC1) Is there any way to improve on it? I'm sure I might be doing a lot of things very inefficiently here. Mat temp2 = input.rowRange(j,j+rowBlock).colRange(i,i+colBlock).t() Mat result = Mat::zeros(1,rowBlock*colBlock,CV_32FC1) Input.at(1,2) = 2 //Makes it easier to verify the correct solution Using that, I've implemented the same in C++ using OpenCV, and although the result seems to be the same, it's awfully slow. I found it hard to understand the implementation present in Octave's source code, so I ran the function on few matrices to understand the logic behind it. I've been trying to implement im2col function present in MATLAB and GNU Octave.