Welcome to this Blog. As you all know Stanford University started an experiment of providing there various Computer science classes online worldwide, and started with first 3 classes AI, ML, DB from last October.
One of the most popular class among them is Machine Learning Class by Prof Andrew Ng. He is undoubtedly doing wonderful job, Excellent way of teaching, so easy to grasp lectures by him made us learn ML so delightful. First of all Many Congratulation and Heartfelt Thank you Sir.
Now, ML is subject involves lot of data churning and Linear Algebra plays a key-role in it to identify the optimum and the best solution for given data-set. Also visualization of same is very necessary to understand it completely that frequently require us to plot the data in various forms. Where Numerical Computing tools like MATLAB comes very handy, but at the same times it requires a pretty good learning-curve and it costs as well. So, Professor suggested us to use Octave as a good alternative, its a very fast processing and easy to learn tool, comes handy with not much heavy load. Also it looks to be a good tool for any proto-typing as Ng suggested. And so far by experience I can attest that.
Well, As a python user, I have got this natural curiosity to test Python Numerical ability to do the similar stuff with its very efficient extensions Scipy & Friends. And i tried to work with that in parallel. And find a good amount of similarity in between both. And in this blog, i'll be posting the Python Conversion of Octave Codes that we'll get as programming exercise (Off-course only after due date).
Well, I'm also a very new user of both so my solution might not be very efficient or the best one expected, but then you are always welcome with your expertise.
One more thing if you are total unaware of Scipy as of now, than i won't suggest to learn Octave and Scipy simultaneously, you may end-up in messy analogy.
So if you are also a enthusiastic python fan or just curious newbie of ML, Welcome to the Blog.
One of the most popular class among them is Machine Learning Class by Prof Andrew Ng. He is undoubtedly doing wonderful job, Excellent way of teaching, so easy to grasp lectures by him made us learn ML so delightful. First of all Many Congratulation and Heartfelt Thank you Sir.
Now, ML is subject involves lot of data churning and Linear Algebra plays a key-role in it to identify the optimum and the best solution for given data-set. Also visualization of same is very necessary to understand it completely that frequently require us to plot the data in various forms. Where Numerical Computing tools like MATLAB comes very handy, but at the same times it requires a pretty good learning-curve and it costs as well. So, Professor suggested us to use Octave as a good alternative, its a very fast processing and easy to learn tool, comes handy with not much heavy load. Also it looks to be a good tool for any proto-typing as Ng suggested. And so far by experience I can attest that.
Well, As a python user, I have got this natural curiosity to test Python Numerical ability to do the similar stuff with its very efficient extensions Scipy & Friends. And i tried to work with that in parallel. And find a good amount of similarity in between both. And in this blog, i'll be posting the Python Conversion of Octave Codes that we'll get as programming exercise (Off-course only after due date).
Well, I'm also a very new user of both so my solution might not be very efficient or the best one expected, but then you are always welcome with your expertise.
One more thing if you are total unaware of Scipy as of now, than i won't suggest to learn Octave and Scipy simultaneously, you may end-up in messy analogy.
So if you are also a enthusiastic python fan or just curious newbie of ML, Welcome to the Blog.
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