[HN Gopher] The Hundred-Page Machine Learning Book
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The Hundred-Page Machine Learning Book
Author : gk1
Score : 158 points
Date : 2021-01-25 17:21 UTC (5 hours ago)
(HTM) web link (themlbook.com)
(TXT) w3m dump (themlbook.com)
| tobinfricke wrote:
| Actual length: 132 pages :-)
| adhoc_slime wrote:
| I was going to joke about false advertising but I suppose a 500
| page book on machine learning is also a 100 page book on
| machine learning.
| mssundaram wrote:
| $structural_vs_nominal_typing_joke
| cambalache wrote:
| I didnt like the book but YMMV. IMO it strikes the WRONG balance
| between length and content.
| nerpderp82 wrote:
| What is the right balance in your view?
| dominotw wrote:
| agree completely. It was like a ML table of contents with brief
| descriptions of each topic.
| moehm wrote:
| If anyone wants to check out, you can read this book for free,
| and buy afterwards.
|
| http://themlbook.com/wiki/doku.php
| jjice wrote:
| Not sure if this was posted by the author, but it wouldn't hurt
| to add TLS and a redirect. Doesn't seem like there's any
| sensitive info coming through, but TLS is so quick and easy with
| Let's Encrypt that it would be a nice addition.
| hyko wrote:
| "Best 100-page book ever!"
|
| Lol.
| 0wis wrote:
| Is this just a disguised ad or a legit proposition ? Any review
| of an HN reader ?
|
| If so, would you recommend this book over online content to a
| beginner ?
| kthejoker2 wrote:
| A beginner from which perspective?
|
| This book is ideal for someone with no knowledge of ML. It is a
| level 100 equivalent book.
|
| Given its mandate of 100 pages, it sacrifices breadth for depth
| - it covers most of its topics in 1 page or fewer. It has lots
| of paragraphs that end with "There are many advanced techniques
| that go beyond the scope of this book."
|
| The best thing about the book is it is fairly comprehensive and
| well-organized as an end-to-end overview. It has short, 1-2
| paragraph chunks about the 80% of ML work that covers 99% of
| what ML is.
|
| It of course doesn't go into detail but it also does not have
| many topics missing, which means if you make a mind map while
| you're reading you'll have a great starting point to branch out
| further once you're done.
|
| When you're done with this book, you should be able to speak
| intelligibly about most aspects of machine learning development
| and design.
|
| You should also be able to find strengths and weaknesses in
| your understanding based on what you came to the table with to
| pursue further.
|
| I think this book coupled with some kind of lab practical or a
| series of Kaggle contests and notebooks to apply what you learn
| in this book would be an excellent "ML 101" course.
|
| I teach out of this book for a 2 week bootcamp to new hires and
| converts to the data science practice at the consultancy I work
| at.
| kthejoker2 wrote:
| I typically recommend a few different books to everyone who
| finishes the bootcamp, based on a self-assessment they take.
| I recommend some books based on their strengths, so they can
| find a career path sooner, and some books based on their
| weaknesses, so they can widen their cone of oppportunity
| within ML.
|
| In our consultancy, data science is done in Python and SQL
| (and PySpark, but I don't hand out books on that during
| bootcamp!), and ML delivery is a combination of math,
| software engineering, and architecture/product owner
| disciplines.
|
| If you're strong in software engineering, I recommend Machine
| Learning Mastery with Python by Jason Brownlee as it's very
| hands-on in Python and helps you run code to "see" how ML
| works.
|
| If you're weak in software engineering and Python, I
| recommend A Whirlwind Tour Of Python by Jake VanderPlas, and
| its companion book Python Data Science Handbook.
|
| If you're strong in architecting / product management, I
| recommend Building Machine Learning Powered Applications by
| Emmanuel Ameisen since it explains it more from an SDLC
| perspective, including things like scoping, design,
| development, testing, general software engineering best
| practices, collaboration, etc.
|
| If you're weak in architecting / product management, I
| typically recommend User Story Mapping by Jeff Patton and
| Making Things Happen by Scott Berkun, which are both
| excellent how-tos with great examples to build on.
|
| If you're strong in math, I recommend Understanding Machine
| Learning from Theory to Algorithm by Shalev-Shwartz and Ben-
| David, as it has all the mathematics for ML and actually has
| some pseudocode for implementation which helps bridge the gap
| into actual software development (the book's title is very
| accurate!)
|
| For someone who is weak in the math of ML, I recommend
| Introduction to Statistical Learning by Hastie et al (along
| with the Python port of the code
| https://github.com/emredjan/ISL-python ) which I think does
| just enough hand holding to move someone from "did high
| school math 20 years ago" to "I understand what these
| hyperparameters are optimizing for."
|
| Anyway, I've spent a lot of time reading and reviewing books
| about ML, and my key takeaway is ones that get you closer to
| writing actual code to solving actual problems for actual
| people are the ones to focus on.
| melenaboija wrote:
| > my key takeaway is ones that get you closer to writing
| actual code to solving actual problems for actual people
| are the ones to focus on
|
| And depending on the industry understand what you are doing
| as it is mandated by regulations.
| denhaus wrote:
| I read this cover to cover. I did not think it was very useful
| for someone who has experience in the field. For a total
| beginner, may be slightly useful.
| Jugurtha wrote:
| What are topics you wanted the book to cover that it did not
| cover? What are the most irritating problems you face in your
| day to day work in the field that you would have wanted to be
| in the book?
| NotPavlovsDog wrote:
| for one of the possible starters, I would go for the MIT
| Licensed "Paradigms of Artificial Intelligence Programming" by
| Peter Norvig. https://github.com/norvig/paip-lisp
|
| a) you get the extra benefit of playing with Lisp b) it gets
| universal praise c) as you guessed, it's free in the freedom
| sense
|
| There are other resources specific to machine learning, but the
| above, from personal experience, actually was fun in the proper
| sense: expanding my mind, knowledge, and providing a well
| thought-out learning experience
| blue1 wrote:
| I loved PAIP at the time, but it's 30 years old now. Is it
| still relevant? anything newer?
| CodeGlitch wrote:
| In some ways I agree with you - Lisp does seem old school
| when all the ML people are using Python.
|
| On the other hand the algos don't change that much so it's
| still relevant in that sense - and the book is about
| getting the reader to become a Jedi, rather than being a
| reference book on AI.
| coliveira wrote:
| PAIP is a great book, but I wouldn't call it ML. It is more
| about traditional AI research based on symbolic computing, in
| the Lisp and Prolog tradition. Modern ML is 90% based on
| statistical and optimization algorithms.
| NotPavlovsDog wrote:
| I recently read a great paper covering a survey on AI. They
| were interviewing AI field experts, and one of the problems
| they encountered were the multiple, often opposing,
| viewpoints, with some experts plain out refusing to discuss
| AI, as they only recognized machine learning.
|
| I suppose for applied ML the approach suggested by one of
| the top comments would be perhaps the most viable - just
| start running code and see where it gets you.
| worik wrote:
| Given that Neural Networks seem to be approaching the
| limits of what they can do it may be time for a return to
| symbolics.
|
| NN at the limit? Is that too controversial? The "black box"
| approach is having difficulty with the last 5% is it not?
| Hence we do not have self driving cars.
| gk1 wrote:
| (OP) A friend highly recommended this book to me and I was
| impressed by the testimonials on its homepage. So I thought HN
| might find it interesting, too. I have no connection to the
| author.
| dtjohnnyb wrote:
| I think of this book as the type of book to give a beginner
| enough info to know what you don't know, or for a professional
| to read before a job interview to refresh your memory on some
| topics you may be using less often.
| iujjkfjdkkdkf wrote:
| Disguised ad. This guy is a social media "influencer".
| rhapsodic wrote:
| And a "thought leader", to boot.
| tbalsam wrote:
| https://www.reddit.com/r/MachineLearning/comments/afy3fk/n_t...
|
| There's some other ones out there...maybe by Hochreiter or one
| of the giants out there? I'd take a look at it, it's a free PDF
| that's absolutely massive and all you need to know for much of
| the basics and a few other deep areas of deep learning.
|
| Or even better... Just pick a project you like and start doing.
| It's like art, you'll learn nothing by studying alone, and
| everything by getting a feel for it and being all excited about
| it and whatnot there. ;))) :))))
| prionassembly wrote:
| Learning anything involves a lot of experimentation, but in
| art the feel you get is very close to what you're actually
| doing, while in math and engineering the "feel" is something
| you learn to interpret with experience applying a theoretical
| point of view to numerous problems.
|
| Even low-grade data science where cobbling together some
| grid-searched scikit-learn stuff suffices is more like fixing
| a damaged combustion engine than riding a bike.
| sillysaurusx wrote:
| "The trick is to read the book first and then decide whether
| it's amazeballs enough to pay the price."
|
| Goddamn, it's refreshing to hear this from the author of the
| book itself. An acknowledgement that book piracy exists, and
| that it's not so bad.
| moehm wrote:
| I think they mean it's free to read here:
| http://themlbook.com/wiki/doku.php
|
| (which is amazing)
| wodenokoto wrote:
| It was recommended in a thread earlier today which often
| prompts other users at their luck at reaching the front page
| with a link submission.
|
| I haven't read his book, but his LinkedIn posts used to pop up
| on my feed until I blocked him because he really sounded
| superficial.
|
| I was quite surprised to see it recommended here. Maybe I
| should give it a look
| kthejoker2 wrote:
| Is there something wrong with a 100 page introductory book on
| a topic being "superficial"?
|
| I find this kind of fixed-mindset gatekeeping a lot and it
| really rubs me the wrong way. Everyone has to start
| somewhere.
|
| We typically discourage people from diving in the deep end of
| a subject.
|
| You know, starting with something "superficial."
| wodenokoto wrote:
| No. I didn't like his LinkedIn posts, so I wrote off the
| book.
|
| Given the attention on HN I am now reconsidering giving the
| book another chance.
| prionassembly wrote:
| People can be tricked into thinking they get more than what
| they actually do. It's flattering to be told you can "get a
| feel for it" and emerge knowledgeable on the other side.
| mikkergp wrote:
| I'm curious to check it out. I find the balance of figuring out
| how long to make texts -- how much supplemental information that
| texts have to be an interesting problem to solve. Most
| educational materials have to much for me, in an attempt to
| appeal to a wider audience, and you can't really learn from a
| cheat sheet or a cram book. Not always because you can't
| understand the material but also because reading a list of facts
| is as dull as paste.
| sengstrom wrote:
| I've read this book recently and found it a very useful overview
| of ML. It is a bit theoretical which is good for somebody that
| comfortably reads math notation. Also the notation is pretty
| consistent which makes connections between methods and algorithms
| stand out.
| michaericalribo wrote:
| > All you need to know about Machine Learning
|
| Eek. This type of extreme claim makes me _even more_ skeptical
| than I already was of such a short overview. Each of the topics
| listed could easily fill 100 pages themselves...
|
| All _who_ needs to know? Hopefully not anyone actually using this
| in their job. Maybe it would be a useful reference for the
| definition of the term, but it 's completely wild to suggest
| you'll have _any_ understanding of SVM, NN, CV, _or_ gradient
| descent after only 100 pages, much less all of them...
| taude wrote:
| If you read the page, there's a great section at the bottom
| titled "Is this book for you?" That pretty much gives you the
| granularity to expect.
| michaericalribo wrote:
| You're right, but not in a good way...that part says it's
| good for
|
| > a software engineer or a scientist who wants to become a
| machine learning engineer or a data scientist
|
| and earlier in the page it says
|
| > you will be ready to build complex AI systems, pass an
| interview or start your own business
|
| I think that's misleading, and just not true.
|
| :(
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(page generated 2021-01-25 23:02 UTC)