The Quotes š
Explaining Metaphysics to the nationā
I wish he would explain his Explanation š
~ Lord Byron (Don Juan: Dedication)
Understanding, n. A cerebral secretion that enables one having it to know a house from a horse by the roof on the house. Its nature and laws have been exhaustively expounded by Locke, who rode a house, and Kant, who lived in a horse š»
~ Ambrose Bierce (The Devil’s Dictionary)
It requires a very unusual mind to undertake the analysis of the obvious š¬
~ Alfred North Whitehead (English mathematician and philosopher)

Deep Learning (Popular) Books: The List š£ š¢
And here we have the two stellar books at which we’ll soon be taking an opinionated look, in turn:
- The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
(Basic Books) by Pedro Domingos š£
- Deep Work: Rules for Focused Success in a Distracted World
(Grand Central Publishing) by Cal Newport š¢
Extra, extra! š£ With the pic above as our guide, hold on to the thought that we’ll later be taking an exclusive, behind-the-scenes tour of how your blogger went about improvising and capturing the photosāthose which adorn the books being reviewedāfor this essay. But all that will have to wait till the end of this essay ā°
Here’s the deal: I’ve received copious feedback from readers to cut to the chase when beginning any given essay. And I am dead serious about honoring the wishes of my readers. So before we get carried away with the inevitable excitement you would expect to accompany an exclusive behind-the-scenes tour, let’s settle for a quick metaphor: Think Monty Python’s Flying Circus, complete with how a propāyep, the one bolstered precariously on the revolving chair in the pic aboveāpainfully fell on your blogger’s foot. You’ll be relieved to know, or maybe not, that your blogger mostly escaped the accident unscathed, though he did cry a bit š Enough said on the whole matter. Let’s dive right into some deep learning goodness š£
Preamble ā±
So what’s up with that? Here’s what: Let’s remind ourselves that the field of deep learning
- …does not exist in a vacuum
- …has deep roots in machine learning (ML)
- …is not a fad
- …is not hype
- …has become inextricably enmeshed with every discipline under the sun
- …requires a different level of abstraction to comprehend it meaningfully enough
- …needs a different mindset in order to run with it and to achieve focused success
I hope the list above suffices to convince you all to rise in unison and thwart all would-be attemptsāpotentially from purists who would violently demur at my selection of books for this essayāat pillorying your blog’s author š± But then again, I only need remind us all that some people can get oh-so-emotional š¹
A long time ago, in a galaxy far away, your blogger used to assiduously write programs in the C++ programming language. But that was before he discovered Java, Scala, Python, and indeed the whole world of distributed computing. And yet there come to occsionally haunt himāespecially right now since we happen to be talking about the admittedly macabre themes of “violently demurring purists” and of “getting drawn-and-quartered”āremembrance of things past, for example this remembrance of his education while working in the C++ trenches:
As Chair of the working group for the standard C++ library, Mike Vilot was told, “If there isn’t a standard string type, there will be blood in the streets!” (Some people get so emotional.) Calm yourself and put away those hatchets and truncheonsāthe standard C++ library has strings.
~ Scott Meyers in More Effective C++: 35 New Ways to Improve Your Programs and Designs (Addison-Wesley Professional).
Lest we digress too much, I hasten to add that, after bidding farewell to the C++ programming community a long time ago, I did make peace with my fellow, hard-core C++ programmers. That was elsewhere in the context of musing on how all languages inevitably carry some baggageāit’s the relative degree of the conceptual burden that a programmer has to bear, when using the model of a given language, and which sets a language apart from others š
Okay, let’s take a deep breath. We’re already done with our first and last digression in this essayāmost of the rest of digressions will be waiting for you once we get to the end of reviewing two sterling books here š
Introduction š
I had initially toyed with the idea of calling this essay “Best Deep Learning Books (Thematic)”. But the more I reflected on (the parenthetical use of the) word “thematic”, the more I realized that we would be entering the realm of “diplomacy”. That realization alone made me cringe and I rushed to replace “thematic” by “popular”.
So while this essay is a variation on the theme of deep learningāhence my toying with the title “Best Deep Learning Books (Thematic)”āI feel that the title “Best Deep Learning Books (Popular)” does a better job of reminding us of what exactly we’re talking about. So there you have it š And that’s what you get this time: “Best Deep Learning Books (Popular)” š
Oh, lest you were confused by this somewhat nitpicky decisionāwhether to use “thematic” or “popular” in the parenthetical rejoinder of the essayāallow me to explain ever-so-briefly what I had in mind in mentioning the “diplomacy” realization above. So it’s been said that
A diplomat is a person who can talk for an hour without saying anything š
Enough said š That, of course, is decidely not my style; I’m the opposite, in that I do my level best, research, get to the heart of the matter, and formulate it to lay the subject matter bare for youāI assume, and hope, that you probably don’t want your blogger to write any other way either ā³
Refresher š¹
Recap of Past Two Essays (In this Series)
We allāmyself prominently incudedāneed refreshers every now and then to load the intellectual goods into our working memory š
Since we went over the background for deep learning in detail in the first two installments to this series of essaysāthis essay of course being the third installmentāwe won’t rehash that context here. At the same time, I cordially invite you to look up those (two) previous essays to refresh you memory on the
And should anyone need proof of the bias for action, and the solid pragmatismānever a purist I was, and never I will beāthat I bring to my daily work, allow me to point out merely that I’ve been spending most of my waking hours designing and crafting distributed systems (using Java and Scala). At the same time, though, the fields of Artificial Intelligence, Machine Learning, and of course Deep Learning never wander far from my mind š
Now this will be a first! I’ve never quoted Quora before; notwithstanding the pseudo-alliteration (neologism alert here, having just blithely used the phrase “quoted Quora”!) it’s appearing more and more regularly on my radar now. In fact, as a long-time (once-passive) subscriber to Quora, I continue to remain impressed by the high quality of their targeted updates (by way of the intriguing emails that continue to land in my inbox, with pointers to Quora questions-and-answers) š¬
Something of Quora
With that preamble, I invite you to have a look at an intriguing question which was posted fairly recently to Quora and ably answered by Andrew Ng (co-founder of Coursera and adjunct Professor of Stanford University). Here was the question š¶
I have a toddler. How should she prepare herself for the job market 15 years from now in the world of AI? (italics mine)
That such questions are even being asked is, to say the least, incredibly encouraging in that this phenomenon underscores the degree to which the quest for harnessing the power of Artificial Intelligence (AI) has permeated the public’s consciousness. In turn, as a clearinghouse of sorts for ideasāwhich the blog you’re reading now is all aboutāI recommend that you look up the full answer via the link that precedes the quote above. At any rate, here is part of the answer (by Andrew Ng) to that question:
Yes, do teach her to code. More importantly, cultivate in her the ability to keep learning.
In the CS world, all of us are used to needing to jump every ~5 years onto new technologies and paradigms of thinking (internet -> cloud -> mobile -> AI/machine learning), because new technologies get invented at that pace that obsolete parts of what we were previously doing. So CS people are used to learning new things all the time.
The thing thatās now changed is that CS has infected pretty much every other industry. So now itās not just the CS world that has to change every few years. Itās that everyone now needs to change. Thatās why being able to keep learning will be the most important career skill you can teach your daughter (italics mine).
Frankly, I was impressed by the delightfully articulate answer by Andrew Ng; again, I encourage to look up the full answer at Quoraāit resonates with the theme of this essay in that paradigms don’t exist in a vacuum; ideas, and indeed paradigms themselves, evolve ever-so-organically š
Any little song that you want to sing
Little songs that you want to sing
Any song will do
Any little song that you want to sing
Little songs that you want to sing
It’s up to you
Little songs that you want to sing
A little song that you want to sing
You’re blue šµ
~ Robert Plant (Lyrics from In The Mood)
1. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books) by Pedro Domingos. š³
One of the Most Fun Books Ever!
This book is proof positive that it’s possible to be fun and substantial at the same time. Before we dive into the goodies you can expect to find in the pages of The Master Algorithm, let’s first hear from Kirkus Reviews, which probably summed it up the best in noting how this book is
[An] enthusiastic but not dumbed-down introduction to machine learning… lucid and consistently informative… With wit, vision, and scholarship, Domingos describes how these scientists are creating programs that allow a computer to teach itself. Readers…will discover fascinating insights.
So let’s please have you squint at the pic above š You’ll notice that The Master Algorithm is sandwiched between two decidedly popular books. Flanking it on the left-hand side and right-hand side, respectively, are:
- Seeing What Others Don’t: The Remarkable Ways We Gain Insights (PublicAffairs) by Gary Klein
- Only Humans Need Apply: Winners and Losers in the Age of Smart Machines (HarperBusiness) by Thomas Davenport and Julia Kirby.
I’m resisting the sorely tempting urge to digress into a word or two on the two intriguing books I mentioned above in passing ā But this time we’re determined to proceed with discipline, with nary a lapse into those digressions. At any rate, if you agree with the basic premise of Carly Fiorinaāformer president, and chair of Hewlett-Packardāin her observation that “The goal is to turn data into information, and information into insight“, then you may find yourself agreeing with my assessment that the themes which these books explore are actually intertwined, and deeply so. Those themes include, but are not limited to
- Seeing patterns where others don’t š
- Handling the tension between fostering human creativity and the relentless march of automation šŖ
- Figuring out how to best manage the very fabric of businessāat least as we know it todayāwhich is undergoing tumultous upheaval š
- Detecting patterns, at both the human and machine level ā·
- Dealing with the catalyst that Machine Learning (ML) is, and which is truly fueling the abovementioned upheaval š
Allow me to remind us allāeven as I ask that you keep the themes above in mindāthat what you want to look for in The Master Algorithm is its unerring focus on teasing out and elaborating how these themes are intertwined.
Plus, having read and re-read The Master Algorithm several times over, I truly felt that its author (Pedro Domingos) did a great job of clearly articulating the book’s themes. His narrative style was methodical and discriminating (in the good sense of the word “discriminating”, I hasted to add lol, in that the narrative nicely delineates and discriminates between the ambiguities that inevitably lurk at the boundaries of related ideas). In the process, I also felt that The Master Algorithm nicely sidesteps the following devilish defintions that conceivably might be applied to books of a lesser meter. I didn’t really say it; just sayin’ š
Discriminate, v.i. To note the particulars in which one person or thing is, if possible, more objectionable than another.
Discussion, n. A method of confirming others in their errors.
~ Ambrose Bierce (The Devil’s Dictionary)
So that’s another cool thing going for The Master Algorithm š
The Joy of Reading this Book
In my mind, The Master Algorithm is to the world of machine learningāand to deep learning in turnāwhat The Joy of Clojure is to the world of programming, and what Joy of Cooking is (was?!) to the culinary world (I can’t definitively judge in the latter matter since my culinary skills barely hover above zero) š
Yes, the former is that good, and let me tell you why. For starters, The Master Algorithm blends an infectiously engaging style with the impeccable credentials of a leading researcher in the field of machine learning and AI: Domingos is a professor of computer science at the University of Washington. He is a winner of the SIGKDD Innovation Award, the highest honor in data science.
A “Map” of the Book
Domingos nicely lays out the map of The Master Algorithm in an introductory section (Prologue) of the book by pointing out how
I had a number of different but overlapping audiences in mind when writing this book. If youāre curious what all the hubbub surrounding big data and machine learning is about and suspect that thereās something deeper going on than what you see in the papers, youāre right! This book is your guide to the revolution (italics mine).
You really should look up The Master Algorithm for the details that Domingos immediately goes on to add to the point aboveāwhich was to describe one kind of its intended audienceāby adding how readers of other diverse backgrounds will find much of value in its pages. In his words, you, too, are the intended audience of The Master Algorithm
- If your main interest is in the business uses of machine learning, …
- If youāre a citizen or policy maker concerned with the social and political issues raised by big data and machine learning, …
- If youāre a scientist or engineer, …
- If youāre a machine-learning expert, …
- If youāre a student of any ageāa high schooler wondering what to major in, a college undergraduate deciding whether to go into research, or a seasoned professional considering a career changeā, …
Again, I’m resisting the sorely tempting urge to digress into a word or two on my use of the word “Map”āas in “A Map of the Cat?”, and as used by one of my heroes in science, the legendary physicist Richard Feynmanāin the heading above š± Your blogger’s will power is being put to a severe test, you all! š§
Let’s Get Ourselves Acquainted
To acquaint you better with the especially valuable aspects of The Master Algorithm, let’s have ourselves a peek at its table of contents š®
Prologue
1 The Machine-Learning Revolution
2 The Master Algorithm
3 Humeās Problem of Induction
4 How Does Your Brain Learn?
5 Evolution: Natureās Learning Algorithm
6 In the Church of the Reverend Bayes
7 You Are What You Resemble
8 Learning Without a Teacher
9 The Pieces of the Puzzle Fall into Place
10 This Is the World on Machine Learning
Epilogue
Acknowledgments
Further Readings
Index
Get to Know the Larger Context in Which Deep Learning Operates
First let’s listen to what one of the world’s most distinguished computer scientists has to say about Domingos’ The Master Algorithm š
This book is a sheer pleasure, mixed with education. I am recommending it to all my students, those who studied machine learning, those who are about to do it and those who are about to teach it (italics mine).
~ Judea Pearl, Professor of Computer Science, UCLA and winner of the A. M. Turing Award
But first, we have here Koller and Friedman acknowledging and paying tribute to Pearl’s inspiring
work š
Much of our core views on probabilistic models have been influenced by Judea Pearl. Judea through his persuasive writing and vivid presentations inspired us, and many other researchers of our generation, to plunge into research in this field.
~ Daphne Koller and Nir Friedman, in the Acknowledgments section of Probabilistic Graphical Models: Principles and Techniques (The MIT Press)
[Geoff] Hinton, a psychologist turned computer scientist and great-great-grandson of George Boole, the inventor of the logical calculus used in all digital computers, is the worldās leading connectionist. He has tried longer and harder to understand how the brain works than anyone else. He tells of coming home from work one day in a state of great excitement, exclaiming “I did it! I’ve figured out how the brain works!” His daughter replied, “Oh, Dad, not again!” Hintonās latest passion is deep learning, which weāll meet later in this chapter. He was also involved in the development of backpropagation, an even better algorithm than Boltzmann machines for solving the credit-assignment problem that weāll look at next (italics mine).
Another thing I liked a lot about The Master Algorithm is that Domingos has clearly taken great care to ensure that the narrative flows smoothly. And take it from me, someone who has been known to read a book or two, and even made an international reputation for himself by thinking once or twice a weekāmuch as I noted in response to a reader comment elsewhereāthe narrative of The Master Algorithm flows seamlessly, like honey. Not mollasses, but honey, to be sure; not viscous, but easygoing and light šÆ
Engaging Style, Comprehensive Coverage, and a Model of Clarity
The second goal of this book is thus to enable you to invent the Master Algorithm. Youād think this would require heavy-duty mathematics and severe theoretical work. On the contrary, what it requires is stepping back from the mathematical arcana to see the overarching pattern of learning phenomena; and for this the layman, approaching the forest from a distance, is in some ways better placed than the specialist, already deeply immersed in the study of particular trees. Once we have the conceptual solution, we can fill in the mathematical details; but that is not for this book, and not the most important part (italics mine).
Bottom line: learning is a race between the amount of data you have and the number of hypotheses you consider. More data exponentially reduces the number of hypotheses that survive, but if you start with a lot of them, you may still have some bad ones left at the end… You can even figure out in advance how many examples youāll need to be pretty sure that the learnerās chosen hypothesis is very close to the true one, provided it fits all the data; in other words, for the hypothesis to be probably approximately correct. Harvardās Leslie Valiant received the Turing Award, the Nobel Prize of computer science, for inventing this type of analysis, which he describes in his book entitled, appropriately enough, Probably Approximately Correct.
Great Sense of Humor
While I haven’t met Domingos in person, yet, I can clearly tell that he’s got a great sense of humor! In a section in The Master Algorithm, where he is regaling us with the crucial role of analogy in machine learning, he begins that section by posing a question to the reader:
Is there anything analogy canāt do? Not according to Douglas Hofstadter, cognitive scientist and author of Gƶdel, Escher, Bach: An Eternal Golden Braid. Hofstadter, who looks a bit like the Grinchās good twin, is probably the worldās best-known analogizer. In their book Surfaces and Essences: Analogy as the Fuel and Fire of Thinking, Hofstadter and his collaborator Emmanuel Sander argue passionately that all intelligent behavior reduces to analogy. Everything we learn or discover, from the meaning of everyday words like mother and play to the brilliant insights of geniuses like Albert Einstein and Ćvariste Galois, is the result of analogy in action.
Rejoice
It’s all in here, folks. Rejoice š¶ Your search for the resource from which to gently learn all about the larger context for deep learning is now officially over. All we to do now is set up camp and start reading āŗ And hey, you do know what I mean, don’t you? I’m not offering any learning campsāat least not at this time anywayājust mentioning the camping metaphor FWIW šŖ
Judgment
2. Deep Work: Rules for Focused Success in a Distracted World (Grand Central Publishing) by Cal Newport š
Appreciating the Guts of Learning (and Working) Deeply
Pardon my admittedly visceral choice of words (“guts” and all) in the subheading above; it was just that I wanted to drive the point home š (This was visceral alert #1). With that, we now come to the second of the two books in this essay. And much as I said at the outset, I’m acutely aware of the gnawing sensation that purists in the field of deep learning would have me drawn-and-quartered for selecting the books for this essay: While the phrase “deep learning” makes a handful of appearances in of these two booksāin The Master Algorithm to be preciseāthat phrase doesn’t so much as make a single appearance in the other book š±
- …does not exist in a vacuum
- …has deep roots in machine learning (ML)
- …is not a fad
- …is not hype
- …has become inextricably enmeshed with every discipline under the sun
Other than the tangential observation that “hope springs eternal in the heart” of your blogger, I trust that the list above suffices to convince you all to rise in unison and thwart all would-be attemptsāpotentially from purists who would violently demur at my selection of books for this essayāat pillorying your blog’s author for not being a purist, I also hope that you’re coming to realize the even larger, global context within which human learning itself operates. We truly need to get a good grip on the ideals we want to strive for, or else risk ending up somewhere else, somewhere on a trajectory where we really don’t want to be š
My choice for selecting Deep Work for this essay, I believe, is justified by the remarkable effectiveness with which it tackles the abovementioned ideas and ideals āµ As participants in the community of readersāwith me serving as your host on this blogāyou are surely not intimidated by taking on some bold and novel explorations, are you? True to the title of Deep Work, to its subtitle, to be preciseāRules for Focused Success in a Distracted Worldāthis remarkable book takes on the ambitious goal of laying bare the gory details of how you, too, can achieve focused success in a distracted world š©
I might as well add: Pardon my admittedly visceral choice of words (“gory” and all) above; it was just that I wished, once again, to drive the point home š (And yes, this was visceral alert #2) š
Riveting Style of Presentation
Deep Work is written in an engaging style and is highy readable. On top of that, it’s eminently substantial. Now how about that for a powerful combination? While I haven’t met Cal in person, yet, I’ve followed his work closely for many years. In fact, it’s not a coincidence thatāand this was many, many moons agoāI had selected Cal for the top spot on my list of top thought leaders. You can read those details elsewhere, but here in a nutshell is how I had introduced Cal:
At the top of my list is Cal Newport, who is just about the most clear-eyed thinker I know of. Cal teaches at Georgetown University in Washington, D.C, where he is an Assistant Professor of Computer Science. What’s unique about Cal are his uniquely original insights, which he shares with the world through his Study Hacks Blog: Decoding Patterns of Success. As the name of his blog signals, his posts seek to capture the essence of achieving meaningful success through wide-ranging, engagingly written, and eminently thought-provoking discussions.
The reason I now mention his academic credentialsāhe earned his Ph.D. from MIT, and graduated from Dartmouth Collegeāis that some of the most elegantly stellar thinking I’ve ever come across is regularly on display in his blogs (italics mineāor as the author of the preceding words, did I even need to point that out? Meta-recursion alert, folks!) š»
Unwrapping and Unpacking the Idea
And don’t worry about getting tangled up in this whole “unwrapping” and “unpacking” business: I’ll digress ever so briefly here, and that purely to illustrate a point, hopefully making the point all the more vivid in your mind and hence memorable. So here’s the dealāmuch as we explored in an essay elsewhereāwe’re going to look at a related metaphor from the Clojure programming language (which just happens to be a Lisp dialect, and I happen to have more than a passing interest in all things Lisp) š
Remember how sequential “destructuring” represents a sequential data structure as a Clojure vector within a let binding? Or how built-in operations support the deconstruction of a syntax object, the composition of new syntax objects from old ones?
Wait, wait, oh wait! I need to get you a more apt metaphor that truly vivifies the notion I have in mind for the “unpacking” business I mentioned above. So let’s rewind a bit and instead refer ourselves to an answer I had offered in response to a reader comment elsewhere š
Likely the greatest hurdle to a deep understanding of what it takes to achieve focused success in a distracted world is that we’re actually swimming in that world. We miss, therefore, the proverbial forest for the trees š²š³š“š²š“š³
Unwrapping and Unpacking the Idea Some More
We need to think at a different level of abstraction, and this is not terribly different from what I think Peter Seibel had in mind when he was explaining in his stellar book entitled Practical Common Lisp (Apress) that grokking macros is impeded, ironically enough, because that theyāre so well integrated into the language. Yep, exactly, and so we miss yet again the proverbial forest for the trees š²š³š“š²š“š³
And rather than leave it dangling, it’s worthwhile to bring the preceding thought to closureānot Clojure, mind you (pun intended, for a change)āso let’s hear Seibel himself telling the reader in Practical Common Lisp about the notion of how š
Perhaps the biggest barrier to a proper understanding of macros is, ironically, that theyāre so well integrated into the language. In many ways they seem like just a funny kind of functionā theyāre written in Lisp, they take arguments and return results, and they allow you to abstract away distracting details. Yet despite these many similarities, macros operate at a different level than functions and create a totally different kind of abstraction (italics mine).
And that’s precisely where Deep Work comes in, offering in its pages the ideasāor “rules”, to use his vernacularāthat are at just the right level of abstraction. That, IMHO, is what makes the book click, YMMV š
True to its title, to its subtitle, to be preciseāRules for Focused Success in a Distracted WorldāDeep Work takes on the ambitious goal of laying bare how you, too, can achieve focused success in a distracted world
Let’s Get Ourselves Acquainted
To acquaint you better with the solidly practical aspects of Deep Work, let’s take a peek at the table of contents:
Welcome
Introduction
PART 1: The Idea
Chapter 1: Deep Work Is Valuable
Chapter 2: Deep Work Is Rare
Chapter 3: Deep Work Is Meaningful
PART 2: The Rules
Rule #1: Work Deeply
Rule #2: Embrace Boredom
Rule #3: Quit Social Media
Rule #4: Drain the Shallows
Conclusion
But guess what? Yep, and while you don’t have to go cold turkey, we’ve got to brace ourselves for cultivating some discipline in there ā©
Oh, did you also notice Rule #4āDrain the Shallowsāand found yourself cringing and reflexively think whether this has got anything to do with “draining the swamp”? Let me assure you, it does not. Again, as a loyal American, I respect the views of readers from all political persuasions; I was simply using poetic license to express mine, and that too in passing ā·
A Definition and a Hypothesis
You will find in the introductory chapter of Deep Work a definition first š¹
Deep Work: Professional activities performed in a state of distraction-free concentration that push your cognitive capabilities to their limit. These efforts create new value, improve your skill, and are hard to replicate (italics mine).
…and then the hypothesis itself š¶
The Deep Work Hypothesis: The ability to perform deep work is becoming increasingly rare at exactly the same time it is becoming increasingly valuable in our economy. As a consequence, the few who cultivate this skill, and then make it the core of their working life, will thrive (italics mine).
Finally, Cal points out with gravitas, and very correctly soāmethinks that the author of Deep Work is on to something really, really importantāwhat the implications of deep work are, beginning with these stark words, reminding the reader that š
Deep work is not, in other words, an old-fashioned skill falling into irrelevance. Itās instead a crucial ability for anyone looking to move ahead in a globally competitive information economy that tends to chew up and spit out those who arenāt earning their keep. The real rewards are reserved not for those who are comfortable using Facebook (a shallow task, easily replicated), but instead for those who are comfortable building the innovative distributed systems that run the service (a decidedly deep task, hard to replicate). Deep work is so important that we might consider it, to use the phrasing of business writer Eric Barker, “the superpower of the 21st century.” (italics mine)
It just so happens that the italicized phrase above refers to something I do for a living. And so it is that I can attest to the verity of Cal’s assessment.
Much like the other book that we explored earlierāThe Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our WorldāCal’s book is filled to overflowing with engaging, relevant, and simply delightful anecdotes that inexorably pull the reader deeper into the vortex of the business of learning how to focus meaningfully.
Standout Feature
Another standout feature of Deep Work is its uncompromising commitment to doggedly pursuing the ins and outs of how you, too, can achieve focused success in a distracted world šŖ
It’s this uncompromising commitment to help the reader that won me over, many times over!
You have to check out Deep Work for yourself. Here, I’ll merely mention in passing some fascinating anecdotes that can light up your experience of reading it and make the book so engaging:
- How Calāa theoretical computer scientist who performed his doctoral training in MITās Theory of Computation groupātalks about spending a decade cultivating the ability to concentrate on hard things. And his intriguing telling of a MacArthur “genius grant” winner who is not on Twitter and who published sixteen papers last year.
- The “grand gesture strategy” in which J.K. Rowling took extreme steps to complete The Deathly Hallows, the final book in her Harry Potter series. “As I was finishing Deathly Hallows there came a day where the window cleaner came, the kids were at home, the dogs were barking,” Rowling recalled in an interview. And the rest is, as they say, history.
- Recollections by Cal of his seven years at MIT, when he worked on the site of the instituteās famed Building 20 and how, in MIT lore, “it’s generally believed that this haphazard combination of different disciplines, thrown together in a large reconfigurable building, led to chance encounters and a spirit of inventiveness that generated breakthroughs at a fast pace…”
Judgment
Dig in to the pages of Deep Work and you’ll get a pretty good idea of why I’m babbling excitedly about it! This is a book for someone who is looking for inspiring, practical, gentleāand frankly, sometimes rather stark, bracing, yet salutoryāguidance on the crucial theme of mastering the core practices that will enable you to achieve focused success in a distracted world. Don’t miss this stellar book š
Connoisseur, n. A specialist who knows everything about something and nothing about anything else š
~ Ambrose Bierce (The Devil’s Dictionary)
I would be most content if my children grew up to be the kind of people who think decorating consists mostly of building enough bookshelves š
~ Anna Quindlen
Philosophy, in one of its functions, is the critic of cosmologies. It is its function to harmonise, refashion, and justify divergent intuitions as to the nature of things. It has to insist on the scrutiny of the ultimate ideas, and on the retention of the whole of the evidence in shaping our cosmological scheme. Its business is to render explicit, andāso far as may beāefficient, a process which otherwise is unconsciously performed without rational tests š
~ Alfred North Whitehead (English mathematician and philosopher)
An Invitation š£
In the end, I invite your commentsāHaving now read the brief take each on the books above š¤
- Do you find that your experience of reading any of these popular books was different? š¢
- Did I perhaps not cover some qualities, and which are the ones that you actually found the most helpful as you learned the larger milieu within which (deep) learning operates? š
- Did I leave out any of your favorite deep learning popular books? š
My hope is that the two brief vignettes will help you in your journey to grokking (deep) learning!
Aha, there it is, flanking the left-hand side of the motley crew (or is it crue?!) of a ragtag smattering of books in the pic aboveāThe Master Algorithmābedecked with a carefully chosen, color-coded army of tape flags š Come to look at it a bit more, the neighboring books of The Master Algorithm, too, are lit up with those trusty tape flags š
You can read up more on “the method to the madness” elsewhereāspecifically in the context of when we had dived into the pages of Algorithm Design and Applicationsāwhere I had quoted Elizabeth Dunn (an anthropologist at the University of ColoradoāBoulder) who, in turn, had been quoted in the wise and scintillating pages of the book The Distraction Addiction: Getting the Information You Need and the Communication You Want, Without Enraging Your Family, Annoying Your Colleagues, and Destroying Your Soul how
Consequently, “I do almost all of my labor and thought-intensive reading in print form.” For me, serious reading involves marking up, underlining, and annotating books; itās a martial art, and it requires the material engagement and support that paper can provide and that screens conspicuously lack. Fifty years ago, MIT professor and hypertext pioneer Vannevar Bush imagined weād do this kind of intensive, interactive, relational reading on the memex, an electronic system he proposed in 1945. Today, people who really have to know their stuff still choose paper (italics mine).
Oh, I nearly forgot to mention, this, too, in connection with the (four) books in the pic above. Did any of those books grab your attention? If so, do please consider posting your comments to let other readersāand meāknow about which one of these gems you would like to hear more about, won’t you? At this time, we’ve already covered one of them (The Master Algorithm) in some depth.
But how about the rest of those books, the three that remain untouched in their pristine beauty ? Do please keep in mind that we all stand to benefit from ongoing dialogs by way of comments on this blog š
My Passion for Sharing š
The business of America is businessš°
Remember, too, the wisdom in these words:
Of making many books there is no end, and much study is a weariness of the flesh
What I will ask of you is to please consider posting your comments to let other readersāand meāknow your thoughts, impressions, observations, anything really: Relax ā± We’re in the comfortable, unencumbered orbit of programming digressions central ā³
After all, and much as I said above, do please keep in mind that we all stand to benefit from ongoing dialogs by way of comments on this blog š
A Digression on the Postage Stamps Theme…
Neither snow nor rain nor heat nor gloom of night stays these couriers from the swift completion of their appointed rounds.
~ Unofficial motto of the of the United States Postal Service (USPS) that’s long been associated with the American postman
Speaking of stamp collecting, I was once also an eager numismatistānow that’s the million dollar word for a coin collectorāso the theme of coin collecting might crop up as the underlying theme in a future essay. A word to the wise šŖ Extra brownie points for the first few readers who spot the coin collecting theme in a future essay, and report their observation by way of a comment or two on this blog šÆ
A Behind-the-scenes Exclusive
We First Set the Scene ā
Finally, we have here an exclusive behind-the-scenes tour of the props for this essay š¬ Much as I mentioned at the outset, where I had invited you to settle for a quick metaphor: Think Monty Python’s Flying Circus, complete with how a propāyep, the prop perched perilously on the prodigious revolving chair in the pic aboveāpainfully fell on your blogger’s foot, though he escaped unscathed, mostly š»
I mean, what’s the big deal with a Bandaid here or there, to heal an injury here or there, right? Oh, if only you knew half the things your blogger does to make youāyes, the thousand and thousands of you who come here every monthāto keep coming back for even more āŗ
Well, here we are, having re-emerged from the dark depths of the deep learning ocean, following our dive to retrieve all the deep learning oysters and pearls we could get our hands on, both past and present. Remember how you had been promised at the very outsetāand here I nudge your attention to the kludgy props and stuff in the pic aboveāa sneak peek into how your blogger went about improvising and capturing the various pics? ā
For one thing, the whole prop setup made me think recursion; yeah, leave it to me to cook up the most interesting connections between a variety of seemingly unrelated ideas and themes. The theme of recursion, in turn, got me thinking to a hilarious and inimitable episode from the Monty Python’s Flying Circus series. That’s where we go next ā·
And Then We Dive Right In š
So let’s have a look at the hilarious meta-recursion going on in the Monty Python’s Flying Circus side-splitting funny episode entitled The Lost World of Roiuramaāin particular the segment “Who’s filming us?”āfrom which I’ll share the following, ever-so-brief dialog in which our four intrepid explorers find themselves hopelessly lost in a jungle. Here, then, are some of the plaintive and frantic thoughts of those explorers š³š³š³š³
FIRST EXPLORER: Wait a moment!
FOURTH EXPLORER: What is it?
FIRST EXPLORER: If we’re on film, there must be someone filming us.
SECOND EXPLORER: My God, Betty, you’re right!
And should you be moved to inquire into the origins of how the name “Python” came to be chosen by Guido van Rossumāthe creator of the incredibly popular Python programming language and known to the Python community as its “Benevolent Dictator For Life” (BDFL)āI invite you to check this out as well in the context of whether Python is suitable for Machine Learning, and indeed Deep Learning itself…
Till we meet next time, you have a great, productive week šÆ
Collage of Pics and Lyrics šø
I’m in the mood for a melody
I’m in the mood for a melody
I’m in the mood
I can make you dance – I can make you sing
I can make you dance – I can make you sing
If you want me to
Any little song that you want to sing
Little songs that you want to sing
Any song will do
Any little song that you want to sing
Little songs that you want to sing
It’s up to you
Little songs that you want to sing
A little song that you want to sing
You’re blue
I’m in the mood, I’m in the mood, I’m in the mood
I can write it on the door – I can put it on the floor
I can do anything that you want me for
If you want me to
I can do it right – I can do it wrong
‘Cause a matter of fact it’ll turn out to be strong
If you want me to
~ Robert Plant (Lyrics from In The Mood)
– As the one-and-only contributor to this blog's content, format, and presentation, Iāas a matter of principleādo not edit my essays after posting them; I will, on occasion, revisit and clean up grammatical mistakes, or perhaps update stale links, but that's about it!
– That was just a friendly reminder…
– I'm here now, following up, to actually add a general comment to my own essay. That sounds a bit recursive, doesn't it? Fancy that š
– Anyhow, here's the idea: Having posted this essay a bit earlier (You're all familiar with the routine of how I write most all my essays over the weekends, or else early mornings, say 4:00 AM to 6:00 AM) the thought crossed my mind that perhaps some recruiters might find this blog and think, heaven forbid, that the essays on the blog are perhaps a glorified pitch of my resume! Sheesh!
– Nothing could be farther from the truth. Yep, exactly! So in a typical essay, I'll routinely clarify up-front my stance on a particular topic, my opinion (personal opinion, which is mine, and mine alone), what my strengthsāalong with my weaknessesāare, what my background is that gives me credentials to talk about any given topic, and so on and so forth.
– And goodness gracious, on realizing that, I immediately hasten to add here the unambiguous thought that I already have the best job in the world. I work alongside some of the smartest people on the planet. More importantly, much more importantly, I work for a genuinely caring companyāa company that cares deeply for its customers and cares equally passionately for its employeesāand there's no lip service going on anywhere. I've been in the industry for a bit over two decades, and know genuineness full well when I see it!
– I'll add that, all employees here, my coworkersāall the way up to the highest levels of managementāsit together at the proverbial table, as it were, like friends. We cheer one another to achieve the very best, every single day, and in the process we seek to usher our industry into higher and higher echelons of excellence and performance. Equally importantly, of course, we doggedly focus on ensuring that our customers benefit from our endless pipeline of innovations, both in the business and technical spheres.
– Enough said!
– Now you all enjoy this latest essay. Let's keep the dialog going, so please don't be shy with your comments š
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– Some essays get tons of comments from readers; last time I checked, the essay on the nuts and bolts of working with Big Data had a whopping 72 comments!
– Others essays make shrinking violets out of you all; evidently, this essay has garnered a grand total of one comment, and that too is a comment from your very own blogger š
– What gives? Well, I dunno… I'm going to chalk this up as one of those abiding mysteries of the universe that one never truly gets to fathom š
– Trust me, though, that nobody's going to make a shrinking violet out of me, no Sir and Madam š
– So here we go, tongue planted firmly in cheek…Ā
– With that,Ā let's revisit a theme we had touched on, in this very essay. It had all started out innocently enoughāplus I'm happy to report that the selfsame innocence remains intact, both of your blogger and of this essayāwith a remark in the essay about how there come to occasionally haunt your blogger the admittedly macabre themes of "violently demurring purists" and of "getting drawn-and-quartered". Ah yes, remembrance of things past, ala Marcel Proust š
– Speaking of remembering, um, remembrance of things past got me all excitedāI think you are beginning to see the recurring theme on this blog where your blogger gets all excited about making connections between seemingly disparate themes and ideas, both technical and non-technical, and then sharing them with you. This time, I got excited about something from theĀ pages of an often-overlooked gem by Dr. Robert Kegan, who is the Meehan Professor of Adult Learning and Professional Development at the Harvard University Graduate School of Education…
– …that superb book by Dr. Kegan is entitled In Over Our Heads: The Mental Demands of Modern Life (Harvard University Press).
– In a nod to the whole business of "getting drawn-and-quartered", Dr. Kegan shared the following hilarious anecdote in In Over Our Heads, and which is what I wished to share with you today, if only to confirm that I'm in good company when I had worried about hopefully-ill-founded worries from the quarters of "demurring-purists-turned-pugilists" lol š
– So in an introductory section of In Over Our Heads, Dr. Kegan shares the anecdote of how
'In my thirties I wrote The Evolving Self, proposing a view of human being as meaning-making and exploringĀ inner experience and outer contours of our transformations in consciousness throughout the lifespan… Some years ago, when I proudly told my father that it was being translated into German and Korean, he said, "That's great! Now when is it going to be translated into English?". And in truth, these fortnightly letters from readers occasionally have a similar theme:
Dear Dr. Kegan, We had to read your book in our psychology class. I can't believe the publishers let the thing out in this condition. No one in our class understands what you are saying. Not even our teacher, and he assigned it! Who are you trying to impress with all those big words? I got so mad reading your book I wanted to come to Boston and break your teeth.'
Sincerely,
[writer's name]
I appreciated the "sincerely"…
And that bringsĀ us to the end of the anecdote…Ā I don't know about you, but I sure got a kick out of that; I trust that you enjoyed it, too, including any and all demurring purists who gleefully read "Programming Digressions" š š
Base upon your thorough commentary, I will certainly read one of these books, Akram. Thanks for the post!
– Thanks, Jeff, for the great feedback. Comments from readers like you make my day, every day š
– Yes, definitely, that sounds good and I hope you'll enjoy reading the book(s). I'm happy that I was able to bring them to your attention…
– To that I'll add (for you as well as, of course, the thousands of visitors who come to the Programming Digressions blog every month), and much as I've mentioned elsewhere on this blog, from time to time, I don't make a single cent out of the links such as those that I provide in the blog (as and when I add references to where all those books are available online, or in bricks-and-mortar bookstores, for purchase, be it at The MIT Press, Amazon.com, BarnesAndNoble, etc.) – I write purely from my passion for sharing with you all what I have learnedāandĀ continue to learnāabout the ever-evolving paradigms in our software development industry, our technological universe, our culture, and everything else in between!!!Ā
– When it comes to blogging, I'm squarely an explorer of ideas; I share with you what's on my mind at any given point in time; I relish doing so, and want to serve you well!!!
– Again, thanks for your comment!
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