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[nursing Books] Epub The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World ✓ Pedro Domingos

Pedro Domingos · 7 Summary

 The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Free download The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World characters The Master Algorithm: How the uest for the Ultimate Learning Machine Will Remake Our World ✓ eBook, PDF or Kindle ePUB Pedro Domingos · 7 Summary Arning algorithm one capable of discovering any knowledge from data and doing anything we want before we even ask In The Master Algorithm Pedro Domingos lifts the veil to give us a peek inside the learning machines that power. Like The core chapters are a good introduction to five machine learning approaches and from that the structural elements of learning The author s description of how he and team abstracted and combined these elements into a synthesized algorithm Alchemy is told in a lively convincing and interesting wayDislike First it s not clear who the target audience is The handwaving opening and closing chapters seem aimed at the non expert However the core chapters demand a basic appreciation of Markov Bayes and probability which the average reader won t have Second the language is idiosyncratic particularly where the author refers to learning a model by which he means training or teaching it logic and structure While I recognize the lack of a good word for this learning doesn t seem right at all I wondered if this was caused by translation from Portugese but either way I think its the fault of Basic Books not doing their job as editors The lack of good editing is a serious detraction and makes key sections far too difficult to read This is noted by other reviewers who take a harsher line but on the whole this detraction didn t diminish my overall rating Drömmen om Elisabeth En novell ur Mord och mandeldoft inside the learning machines that power. Like The core chapters are a good Sunshine on Putty introduction to five machine learning approaches and from that the structural elements of learning The author s description of how he and team abstracted and combined these elements The Oxford Library of Practical Theology into a synthesized algorithm Alchemy The Secret of Happy Parents: How to Stay in Love as a Couple and True to Yourself is told De Cómo Romeo se transó a Julieta in a lively convincing and Man of Honour John MacArthur Duellist Rebel Founding Father interesting wayDislike First Saga of Recluce Books 6 9 The Saga of Recluce #6 9 it s not clear who the target audience خاطرات و اسناد ناصر دفتر رواییانقلاب مشروطیت، نهضت جنگل، وقایع سیاسی و اجتماعی خلخال is The handwaving opening and closing chapters seem aimed at the non expert However the core chapters demand a basic appreciation of Markov Bayes and probability which the average reader won t have Second the language Little Box of Style is The Enthusiastic Amateur idiosyncratic particularly where the author refers to learning a model by which he means training or teaching Black Blade\Angel Eyes it logic and structure While I recognize the lack of a good word for this learning doesn t seem right at all I wondered The Toybag Guide to Canes and Caning Toybag Guide if this was caused by translation from Portugese but either way I think The Whispering Room Jane Hawk #2 its the fault of Basic Books not doing their job as editors The lack of good editing From Trauma to Enlightenment self therapy in twelve steps is a serious detraction and makes key sections far too difficult to read This The Arabs is noted by other reviewers who take a harsher line but on the whole this detraction didn t diminish my overall rating

Free download The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Free download The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World characters The Master Algorithm: How the uest for the Ultimate Learning Machine Will Remake Our World ✓ eBook, PDF or Kindle ePUB Pedro Domingos · 7 Summary Google and your smartphone He assembles a blueprint for the future universal learner the Master Algorithm and discusses what it will mean for business science and society If data ism is today's philosophy this book is its bibl. Pedro Domingos has to be given huge credit for being the first person to attempt a popular science type book on the subject of machine learning This is by no means an easy task there is a lot of material to cover in this area and some of it is complex That said I think this book could have been a lot better I did actually like the author s writing generally but I felt the book was poorly structured the technical explanations in particular were poor and after the final chapters I was left wondering if the entire book was less impartial than the prologue and early chapters might have lead me to believeWhilst I think there s stuff in the book that could be improved it s hard for an author to make those kind of improvements without feedback I can t help but think that the input of editorsagentspublishers should have made this a much better read It seems to me that this book was crying out for a ghost writerco author experienced in popular science writing More importantly I think it desperately needed proof reading by people unfamiliar with machine learning My guess is that drafts of this book were read primarily by people highly experienced in this subject ie people who already understood the material being presented I m really struggling to see how someone at Basic Books could have read the book prior to publication and thought Yep that was relatively easy to follow The prologue claims the book is intended for a wide range of readers pretty much from novice to expert Personally I would suggest that the only people who won t struggle with this book are experienced industry based practitioners and academicsresearcherspost docs in this area and I guess also readers who are happy to just skim sections they don t understand To give some perspective I have a grasp of ML basics I currently work in a ML group although my research is not explicitly ML I ve taken introductory and advanced masters units in the subject and have implemented some of the algorithms myself Yet I struggled to understand much of this book both the specific details and the broader overviewI may reread some of this book in a couple of years when I have a bit understanding and experience under my belt Perhaps then I will appreciate the details and the author s perspective better At my current level of experience I found the book very hard going a frustrating read and I don t feel like I learnt very much from it For the time being I will stick to machine learning text booksSpecific comments in no particular order mostly negative sorry Key fundamentals of the subject are either not explained eg hypothesis as an ML term poorly explained eg genetic algorithms are explained devoid of any mention of selection or left till way too late in the book eg supervised v unsupervised learning left till p203 This latter example is a real shame in my opinion as I found this to be one of the best written chapters of the book Many other areas did not get the foundational explanations needed prior to developing ideas further Example The most important uestion in any analogical learner is how to measure similarity This crucial uestion is raised then frustratingly left unaddressed because the subseuent text lacks any specific coverage of how this may be actually achieved I felt that hypothesis feature attribute label example should have been explicitly defined in the context of what they mean for ML right at the start of the book This would have given the reader a much firmer footing before seeing further explanations using these terms routinely Non ML readers will not appreciate for example the very specific use of hypothesis wrt discussing ML algorithms The first few pages of the prologue has been described by another reviewer as IIRC evangelistic nonsense It s a harsh comment but it s pretty much on the nail The author is right to highlight the role ML already plays in our world but he s overstating the case for most people almost to the point of ridiculousness Most people s lives aren t like this even in computer science It s not an inspiring start to the book In the first 3 chapters very little happens I think this needs some significant condensing or better replacing with a chapter that covers fundamentals gives some sense of supervised vs unsupervised learning and talks about how to measure similarity As it is I had to wait to page 93 before we got started on ML proper The book is crying out for some diagrams Of the few that are used some could have easily been better The important diagram illustrating the five tribes is neither intuitive nor informative And why when illustrating SVMs would you not show the margin in the diagram Thus immediately giving an intuitive idea of the role of the support vectors and also differentiating SVM from the simpler classifier diagram There are places in the book where if you re anything like me you ll be tearing your hair out for want of an explanation In describing how nearest neighbour works simply and only saying that It consists of doing exactly nothing is incredibly unhelpful especially when the next few pages go into the algorithm in detail in the absence of a foundational explanation of how it works Whilst in general I liked the writing style at times I found phrases and expressions used by the author to be pointlessly obscure thus confusing the reader further rather than clarifying Two examples i the heading One if by land two if by Internet is probably baffling to the majority of people who won t be aware of the original American War of Independence phrase it s derived from and especially so for readers outside of the US and so it doesn t help bring focus or coherence or clarity to the subseuent text that it introduces ii As Isaiah Berlin memorably noted some thinkers are foxes they know many small things and some are hedgehogs they know one big thing Metaphors are a great way of using an analogy to re frame a difficult idea in familiar terms They can thus render something complex or intractable as immediately intuitive This doesn t work if the chosen metaphor is as unfamiliar or obscure as the concept it is supposed to explain S curve this term used throughout the whole book In a book at this level what s wrong with just calling it a sigmoid We routinely learn MLNs It s a trivial point and I imagine I ll be accused of being a grammar pedant but this occurs than once in the book and I am sure other people will cringe at it The correct word if you don t want to use teach is surely train or alternatively rearrange along the lines of we let the algorithms learn or the algorithms learned Chapter 9 i I wasn t particularly enthralled by the storytellingfable approach the author used here That s just my personal view others may like it ii What particularly bothered me here was that suddenly it seemed that the book was no longer a general introductionoverview of machine learning and a way to promote the author s own area of research There is nothing wrong with an author presenting a partisan perspective but I would have liked to have had a better sense that this was what was happening from the start as the book till then had seemed like a relatively impartial broad coverage of the field experienced readers may have a different take on this iii The failure to convey an intuitive sense or simple understanding of the various algorithms in earlier chapters of the book meant that when they all were all bundled together in one chapter I became totally lost For me this chapter was a total train wreck Chapter 10 presents an interesting but somewhat rose tinted view of an ML future The author is clearly much better informed than I in this area but I still think that this could have been a little bit balanced I enjoyed the ideas but the whole chapter neatly sidesteps the important elephant in the room that advanced MLs are inevitably likely to end up in the hands of the most powerful at least initially rather than the most benevolent or philanthropic I also felt that there were a few occasions in this chapter where he was presenting his personal opinion as fact a personal bugbear of mine Examples i technology develops as an S curve aaargh rather than exponentially ii that we are at the end of Moore s law again I particularly liked the adapted uotes presented in this chapter and the subseuent epilogue any sufficiently advanced AI is indistinguishable from god and the unexamined future is not worth inventing nice touches but only in those cases where it doesn t confuse if the reader doesn t get the reference The book has a good reading list at the back albeit with a few surprising absences however frustratingly the book does not cite sources in the main text for when specific topics scientific work are being discussed The index is excellent

Free read æ eBook, PDF or Kindle ePUB · Pedro Domingos

Free download The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World characters The Master Algorithm: How the uest for the Ultimate Learning Machine Will Remake Our World ✓ eBook, PDF or Kindle ePUB Pedro Domingos · 7 Summary A thought provoking and wide ranging exploration of machine learning and the race to build computer intelligences as flexible as our ownIn the world's top research labs and universities the race is on to invent the ultimate le. AWESOME OVERVIEW OF ML IDEASI have read a few textbooks on machine learning intro to Statistical Learning by Hastie etc and so I would say that my knowledge of ML is at the textbook overview level Since I am not a ML practitioner I may not be the best judge of a book such as this one it was a fairly difficult read and I know I need to read the book a second time to get an even greater appreciation and understanding of the concepts covered by the author That being said it was a very enjoyable book The book was very different from any ML book that I ve read or checked out either at a bookstore or online I think one needs to have some knowledge of ML to appreciate the book concepts like supervised learning unsupervised learning Bayesian inference support vector machines neural networks etc The book deserves a 5 star rating because it added a lot of value to my understanding of ML and increased my desire and curiosity to learn about the field of ML