Would be nice to have something like bookmarking in chat; like you can pop back to a mention but then permanently. @koustuvsinha yes I would be interested too; as always to learn from it.

@erictleung thank you I was just brushing up my skills as Javascript tends to overflow them haha like holiday when you say one sentence with Dutch, English and German together.

mesmoiron sends brownie points to @erictleung :sparkles: :thumbsup: :sparkles:

:cookie: 417 | @erictleung |http://www.freecodecamp.com/erictleung

@evaristoc already saving my seeds; just to keep them out of the hands of multi nationals in case the English seed bank perishes. Chat services; I still don't know why humans are so eager to use chat bots. For simple questions and queries fine; Our town has Ally. Like the faqs on the internet and siri. However I found siri annoying because it could not sense my working routine. When to bother me asking or not. But those interactions make you think really what interactive communication is really about.

@albert2309 quantifying human values I think that is interesting too. However values can be derived from what is said or written, but also from what is avoided or not written. That space can be huge. As long as you miss out on detailed life experience it is hard to do it accurately. New York Times had I believe an article on that. Lastly I like camperbot because it is distinctly a bot; I would not be sure if people like a bot that is identical to humans. It falls almost in the category of transgenders; the amount of people who set the changed gender equal to all specific details is much lower than accepting it for the sake of other having well being in their lives. And that is hidden from ML as for now. Complexity is about details, details I think; even tiny details count.

@albert2309 quantifying human values I think that is interesting too. However values can be derived from what is said or written, but also from what is avoided or not written. That space can be huge. As long as you miss out on detailed life experience it is hard to do it accurately. New York Times had I believe an article on that. Lastly I like camperbot because it is distinctly a bot; I would not be sure if people like a bot that is identical to humans. It falls almost in the category of transgenders; the amount of people who set the changed gender equal to all specific details is much lower than accepting it for the sake of other having well being in their lives. And that is hidden from ML as for now. Complexity is about details, details I think; even tiny details count.

"Foundations of Data Science" https://www.cs.cornell.edu/jeh/book2016June9.pdf Flipping through the text, looks to be a very math heavy book on data science. Here's an excerpt from the Intro.

With this in mind we have written this book to cover the theory likely to be useful in the next 40 years, ... One of the major changes is the switch from discrete mathematics to more of an emphasis on probability, statistics, and numerical methods.

@erictleung a wow! book... excellent exposure of some elemental probability distributions from a more geometrical point of view and sampling theory. Unusual: normally the emphasis is purely analytical. This approach is more from physics. Are the authors physicists? This approach is also very useful to understand multidimensionality, which is the key of many Data Science problems and understanding random point generators.

All the first part of the book emphasises parametric statistics, particularly Gaussian.

The same approach (Gaussian, Vector Space, etc) goes for the second chapter but this is not unusual. SVD is a VERY important transformation, and it is key for many other ones in parametric statistics.

Then, for the graph part, some welcome combinatronics. Good! The random generation of triangles in a graph and then the derivation of the variance of a graph based on them is*wow* for its simplicity! Excellent. And then transitions... those guys are physicists... This is the chapter I am more interested...

Analysing Markov theory after graphs is a BIG plus. Definitively a approach based on space representation. @erictleung: relevant for you if you want to go Biotech this chapter.

And it is not until all that theory that you start the Machine Learning part... OK!!! Good! Ending with algorithms for big data...

@erictleung this book is a PIECE OF WORK. If you have ever the money for the finished book, BUY IT! Pity doesn't consider some programming exercises, but it is a well written book.

All the first part of the book emphasises parametric statistics, particularly Gaussian.

The same approach (Gaussian, Vector Space, etc) goes for the second chapter but this is not unusual. SVD is a VERY important transformation, and it is key for many other ones in parametric statistics.

Then, for the graph part, some welcome combinatronics. Good! The random generation of triangles in a graph and then the derivation of the variance of a graph based on them is

Analysing Markov theory after graphs is a BIG plus. Definitively a approach based on space representation. @erictleung: relevant for you if you want to go Biotech this chapter.

And it is not until all that theory that you start the Machine Learning part... OK!!! Good! Ending with algorithms for big data...

@erictleung this book is a PIECE OF WORK. If you have ever the money for the finished book, BUY IT! Pity doesn't consider some programming exercises, but it is a well written book.

@erictleung Any of them are physicists! All are PURE CS!! And very well known. This is tremendous book in my opinion. It is going to be the next bestseller in this sector.

@evaristoc will keep this book under my pillow. Maybe some knowledge will diffuse into my brain :smile: