MatLab /Octave machine learning: examples of algorithms supported by formulas

 3r395. 3r3-31.
MatLab /Octave machine learning: examples of algorithms supported by formulas 3r3384. 3r375.  3r395.
I recently started learning machine learning. I started with the beautiful, in my opinion, course by Andrew Ng. And in order not to forget, as well as repeat the learned, I decided to create a repository of 3r310. Machine Learning in Octave
. In it, I collected mathematical formulas for hypotheses, gradient descents, "cost function", sigmoids and other fundamental "machine" for machine learning. Also added there are simplified and refined examples of the implementation of some popular algorithms (neural network, linear /logistic regression, etc.) for MatLab /Octave. I hope this information will be useful for those of you who are planning to start learning machine learning. 3r3384. K-means algorithm with an example of the distribution of data into three clusters.
3r350. Search for anomalies using a Gaussian 3r382 distribution. with an example of defining an overloaded server.
3r3355. A neural network based on a multi-layered “perceptron” 3r3-382. with an example of handwriting recognition.
3r375.  3r395.
For each algorithm, there is a 3r363 file. demo.m from which you can start the analysis of a single algorithm. When you run this file from the Octave console (or from MatLab), the service information will be displayed illustrating the operation of the algorithm, as well as graphs will be built to help you figure out which training set is in progress. 3r3384. 3r375.  3r395.
3r3384. 3r375.  3r395.
I hope this repository will be useful for you and will help you take another step towards machine learning. 3r3384. 3r375.  3r395.
P.S. The examples in the repository are for 3r379. MatLab
/ Octave . This may not be such a popular option now as Python, but still it might be a good idea for learning, rapid prototyping and the same matrix multiplication without additional plug-ins and libraries. Once again, successful coding you!
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