I'm Andriy Burkov, the author of the Amazon bestseller The Hundred-Page Machine Learning Book. AMA!

If your book was titled as ‘The Two-Hundred-Page Machine Learning Book’ what would be the additional topics?

I would describe generalized linear model, generative adversarial networks, and LambdaMart and metric learning in more detail. I would also explain the temporal unfolding of a recurrent neural network.

it's impossible! This sort of additional material takes more than 100 pages for a reason!

Nice try! Maybe in the second edition.

I would also explain the temporal unfolding of a recurrent neural network.

You can do that?

I mean, I would explain how a recurrent neural network with only one layer becomes a N-layer neural network when the length of the input example is N.

Can you prove you are the real Andy Burkov and not a machine?

Sure: https://bit.ly/2RWGmcb

Holly shit, I remember this! My disdain towards him started that day.

I don't. Could you enlighten me?

https://www.reddit.com/r/IAmA/comments/1c5zxh/i_am_morgan_freeman_ask_me_anything/

I think a GAN would have made a much cleaner Photoshop job. Proof accepted!

Haha, true!

You are smart. You uploaded a draft version of your book to torrent sites as to avoid other people uploading the final version. When one downloads the book and after noticing that it is just a draft version, and an annoying one at that, they are more likely to buy the real thing. [EDIT: I see that the draft version is also the one you allow downloading on a per-chapter basis, so perhaps it wasn't you uploading to the torrent sites.]

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If we buy the paperback, do you also send the .pdf/epub file?

You uploaded a draft version of your book to torrent sites

Haha, I didn't do that. I only uploaded drafts to the book's official website and they are still there and will always be. In fact, those drafts are updated regularly based on feedback from the readers, while paperback/kindle books don't. However, once a critical mass of manuscript updates will be made, I'll update paperback/Kindle too. Thanks to print-on-demand, even the paper book can be updated.

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> If we buy the paperback, do you also send the .pdf/epub file?

PDF yes because it's identical to paperback. I send it on demand upon receiving the proof of purchase.

As for epub, I have put a significant additional effort in building a quality epub, so, unfortunately, you will have to buy it.

What's the difference between PDF and epub? I've always wondered and obviously you're good at explaining things...

PDF was designed to look exactly the same independently of the device or the screen. EPUB was made to be adaptable to the screen and to the reader's preferences for font, size, colors.

You ever attempt to read a PDF book on your phone? You have to zoom in and it's overall a pain in the ass. Epub (and any epub reader) will allow the text to "reflow" to your phone's screen size, so you don't have to attempt to zoom in on the page itself.

I have actually built the PDF versions optimized for two most popular smartphone screen aspect ratios, with larger font and repositioned images.

You said build. Did you by any chance use LATEX ?

I used markdown and LaTeX combined. Pandoc transforms such a hybrid markdown into pure LaTeX and then I build the final pdf using pdflatex.

Right. Secondary question. Can I send you the money for the book, but only receive the pdf? I’m not very good at actually opening a book, so I feel like owning the physical copy would do me no good.

Sure. You can buy the PDF on Leanpub.com for example.

Hi Andriy, your book is pretty good, it is very clear. What kinds of skills do you think is essential for the people who work in ML or Data Science in the future?

When I receive a resume, besides demonstrated knowledge in ML, I look in the resume for two things: 1) education or experience in computer science/software engineering and 2) creativity/unordinary thinking.

How do you quantify or notice creativity through a resume? Is it through their projects and way of tackling the problem statement or something else?

Unordinary things, like build a drone that can navigate itself based on GPS signal, or writing a book, or self-taught playing guitar, etc. All unordinary situations are different.

Wow, this gives me hope that my small electronics hobby actually means something.

It does! Everybody who tells you otherwise is a moron.

This is really interesting. I would have never thought to put self taught guitar or other hobbies on a resume. Thinking of it now though, it would be something that would stand out if I read it when reading resumes.

I confirm that as a hiring manager who doesn't hire ordinary people to do unordinary work :-)

self-taught playing guitar,

Guess I should start putting that on my resume.

You definitely should.

How can people who are not in these fields (e.g. those in business, arts) get to know ML?

The high-school math is enough to follow my book. In Chapter 2, I explain the notation and basic math and stat concepts. There are also online courses that explain the math behind ML.

Hi Andriy,

Your 100 paged ML is an excellent piece by all standards. I am pretty sure I may be the first to purchase from Africa (will do so soon) should you check your demographics.

ML has taken the forefront in many applications lately, including health and Economy Sharing. In developing countries, how do you think ML could be tuned further to support common disruptive sms platforms to influence critical sectors to reach the masses? For example, sms is a major conduit to reach the majority of people, because phone numbers have almost become identity and online/offline passes, be it ecommerce or fintech transactions because generally fancy apps wont do the trick in this part of the globe, at least for now. For this reason, top notch phone verification and SMS/voice OTP platforms such as RingCaptcha (ringcaptcha.com) have proven to be a major catalyst to further scaling disruptive technologies (that need to identify and contact real users) that are supporting the masses here in meaningful ways. How, in your opinion, can ML supplement great platforms like RingCaptcha, to further improve the positive catalytic effect that such 2FA and sms/voice OTP technologies bring and how can they be documented in compressed materials like your great ML for the developers out here to tap into? Thanks!

Oh. It's a big question and honestly, I don't know the specifics of technology used in Africa. I heard that people use sms to navigate the internet, but that's it. Assuming that SMS is quite short, I think that AI could help to bring the answer directly to the phone instead of returning links (the way Google works).

Hi Andriy, there is a lot of hype around the social impacts of machine learning ("AI"). For example and in short, Zuckerberg says AI will make everything wonderful and Musk says AI is an existential threat to humanity. From your perspective, what do you think will be the specific aspects of ML/AI that can contribute positively to society (and how) and where do you think legislators need to (urgently) step in?

In my opinion, the positive impact will come from better services (online and offline) for consumers and more creative/clean/secure work for the workers.

On the negative side, I personally enjoy Google search quality, but don't like that the websites are now made for Google bot in the first place and to the human in the second. A more serious problem is the machine-assisted hiring process in which the machine ranks candidates according to the relevance to the job description, or even filter our some candidates automatically. It reminds me of the future in the Gattatka movie and it's scary.

don't like that the websites are now made for Google bot in the first place and to the human in the second

Hasn't this always been a problem of websites gaming their sites to benefit from problems with the Google search algorithms. Iirc, websites used to just put in random keywords in and have wonky titles filled with keywords until Google tweaked their algorithms. Seems like it will continually be a cat and mouse game that gets progressively harder to game.

It was always like that. But Google used (and Stanford taught) machine learning way before everyone started to talk about it.

Few questions:

1) What mathematics background do you need to read your book?

2) What level of mathematics should the ML practitioner know?

3) Related to 2, but this question is more targeted at the research level. What areas of mathematics should someone master to do research in Deep Learning? Are areas like Real Analysis, Functional Analysis, Measure theory necessary? Or some other areas not mentioned here..?

1. High-school level. Plus I introduce all necessary in Chapter 2.
2. That's hard to say. In practice, you don't use math in your work that much. However, if you do, you can build something nobody can.
3. Deep Learning research right now is mostly experimental (with some rare exception). People try this, then try that. Then when it works they publish. So the more you have GPUs the faster are your "try this try that" cycles.

I can answer the first one cause he mentioned in another comment you'd be ok with a highschool math level for the book. As for the other questions, if he doesn't get around to it i do have a friend who does machine learning in Germany for a university who i could ask about it.

Thank you :-)

Can I use your book to deliver a working implementation of ML? Or is it more for understanding the concepts?

Some algorithms, like decision tree/random forest/gradient boosting/knn and even neural network learning definitely yes. For some others, like HDBSCAN and UMAP, you will have a good understanding of the principles, but you have to read the original papers and even dig in the source code of those algorithms if you want to implement them yourself.

Hi Andriy,

I'm going to do my Master Thesis about anomaly / outlier detection in transactional data, consisting out of 100.000 up until 1.000.000 records. The goal is to be able to detect anomalies which can be an indicator of fraud. The features of the data mainly consist out of categorical data and a few (two) are numerical. Can you recommend an algorithm or technique on approaching this case?

The main techniques I came across are: Autoencoder neural nets, K-NN, One-class SVM, Principal component analysis, Isolation forests.

Furthermore one specific algorithm for categorical data: K-Modes.

Most algorithms require me to transform the data to numerical data (embeddings / one-hot). Maybe you can recommend me a good approach I haven't read about.

I've asked about this problem before on another topic in this subreddit and people also recommended me a Bayesian approach, but I haven't checked this out. I don't know much about Bayesian approaches. Do you think it can be effective in outlier detection in mainly categorical data?

Thank you in forward! By the way, I really liked your book!

I would start with an autoencoder right away. The principle is to encode a transaction data and then try to reconstruct it. If the reconstruction is very different from the input, then it might be an outlier. One-class classifiers might work too. I, however, tried them in one project without luck.

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I wouldn't worry about the conversion of categorical data into a one-hot vector. It's a standard procedure and it works fine.

Sounds like an interesting book. I'm curious about what you did differently to reach your target number of pages. Less algorithms? Less background? A completely new approach?

I tried to explain things as simple as possible. If I could write one sentence instead of two, I tried to do that. One of the epigraphs in the upcoming hardcover edition of the book is:

"If I had more time, I would have written a shorter letter." --- Blaise Pascal

Hey Mr. Burkov,

Thanks for doing this AMA, otherwise I probably wouldn't have found out about your book. I was thrown head first into ML for a class, and needed to teach myself enough to code a GAN within a semester. Hopefully your book can teach me some fundamentals better than slogging through textbooks above my knowledge level, or trying to speedrun online courses meant to span months.

All this inspired me to pursue a math minor, because I found the math behind ML really fascinating. I'm kind of stressing over having a career in ML, though. On that note, my questions:

1. What do you consider the most valuable asset for a ML career? (Graduate degree, paper, side projects, experience, etc.)

2. For a job in ML/ Data Science, what knowledge separates a great candidate from an average one?

3. In a similar vein, what the first question you would ask in an interview to judge if someone knew their stuff?

What do you consider the most valuable asset for a ML career? (Graduate degree, paper, side projects, experience, etc.)

I would say proven curiosity supported by good computer science background. For my team, I like the resumes in which the candidate participated in some DIY project, or made a technical master's, or just made internships in some R&D labs.

For a job in ML/ Data Science, what knowledge separates a great candidate from an average one?

A great candidate is capable of answering the "why" questions.

In a similar vein, what the first question you would ask in an interview to judge if someone knew their stuff?

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- Explain why this thing work?

or

- If I change this that way, will it still work?

Apart from your book, what are some other books that you recommend for a beginning Machine Learning user?

Any software that you recommend?

I think that the book of Aurélien Géron (ML with scikit-learn and TensorFlow) is very good. I personally started in ML with the book Data Science: Practical Machine learning techniques and tools which I liked (except for the second part about Weka which nobody uses).

The software for a beginner is of course scikit-learn. If you want to learn to train neural networks, I recommend starting with PyTorch, not Keras/TensorFlow.

Why does no one use weka? I was taught some basics with this tool but was never really introduced to any others.

It has a very specific interface, file formats, etc. Compared to more modern scikit-learn, it feels very dated. And it's also quite slow compared to modern Java implementations of ML algorithms, and even compared to Python implementations based on numpy/scipy.

Where do you see Machine Learning Education in 5-10 years?

I think it will become a standard part of any computer science graduate program.

To what extent do you think that all this interest in machine learning is a fad that will go away soon?

I think it's here to stay. Almost nothing you can read about (in my book or elsewhere) was invented in this decade. Most of the algorithms we use come from the 70s-90s of the XXs century. What's different today is the availability of data to make those algorithms really useful. I don't think that this amount of data will go anywhere anytime soon.

I am doing my masters now and I want to specialize in Bayesian techniques in graphs and risk analysis. What do you think of the future applications of Bayesian methods? Do you use them in your work?

If you talk about graphical models, they aren't my strong side. Unfortunately, in my practice, I haven't encountered any need to use them. (Here someone has to say that neural networks are graphical models too, so let it be me.)

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The only book on graphical modes available (Probabilistic Graphical Models: principles and techniques, which I have) is, in my opinion, poorly written. Again, my personal perception is that the goal of the authors was not to teach the reader, but to show to the world how much text they can write on the topic.

Do you have any serious plans of writing another book? If so, about what?

I started thinking about it right after I finished my first book. However, right now I have to solve several publications riddles, including producing an English language book for the Indian market. It's complicated because I don't want to compromise quality just to sell more books.

As for the topics of my potential new book, right now the only two ideas I have are "the hundred-page text processing book" (less interested personally, but get requests from readers) and a book on the most important algorithms for our civilization and modern lifestyle. For example Fast Fourier Transform, PageRank, Simplex Method, some search, sorting, and string matching algorithms, etc.

Hi Andriy,

I have been a fan of your posts on LinkedIn for a while now and wanted to ask your opinion on the platform.

In the 2 1/2 years of using LinkedIn significantly, I have seen an explosion of communities developing around discussing and promoting the use of machine learning within both academia and business applications. At the same time, I see that this has brought about some clique groups forming, with the content being shared more focused on generating likes than being insightful.

What do you see as the biggest positives and potential threats of LinkedIn with the ML community over the next few years?

That's a great question. Me personally I don't participate in any group as you can see from my activity on LinkedIn. However, I agree that there are groups of people that benefit from one another's audience to increase the visibility of their content.

LinkedIn as a platform has a great potential. However, right now it's not considered by real ML professionals or academics as a valuable platform. As a result, we have a huge audience that looks for high-quality content and very few "influencers" that attract most of the content views. Some of those influencers are quite ordinary, but because people have nothing to compare with, they trust the opinion of those influencers.

So, on the positive side, we have a huge audience of talented and aspiring professionals. On the negative side, we have a lack of really professional community leaders, which I consider as a threat.

I think Microsoft has to put more effort into promoting LinkedIn as a professional/technical/scientific content network.

Can you go into the details of why you decided not to get into Reinforcement Learning in the book?

I followed the same paradigm that most ML books follow: classification/regression apart, reinforcement learning apart. I think it's reasonable, because reinforcement learning is quite distinct from the rest of the ML from the algorithmic and even notation standpoint. This might confuse the reader. The AIMA book covers both in about 1000 pages, I had only 100 :-)

Hi Andriy, first off want to thank you for making your book available for free and then making it available at low cost on different platforms. I've purchased an ebook on leanpub to show my support. My question is concerning grokking machine learning. I am not in a rush to learn or use ML but have been learning about it through books like "elements of statistical learning" for a few years now. Unfortunately I have not got that much out of it, probably due to my weak mathematics level. Can you suggest a study path for someone working full-time to grok ML (as covered in your book) given let's say 6-18 months? Perhaps starting from mathematical foundations. Thanks!

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PS: If you have personal favorites/recommendations of math reference books I'm sure many people would be interested to know as well.

I have seen recently a couple of books called "Mathematics for machine learning". Start with those. There's also an online course on Coursera with a similar name.

As for the path. I think it's mandatory to learn programming, so buy a good book on Python and read them, try to do exercises. Then read my book (I don't try to promote it, you already have it :-). Then, once you start feeling confident in programming and ML theory, try to participate in a Kaggle competition. Don't necessarily try to win, just get a feeling of how real work in ML is being done.

Sounds like good advice. For me I’m actually a professional python programmer at a tech company. Should I try and implement the algorithms in your book? Or just start trying Kaggle

You can try those from my book. You can reference implementation on Github to compare with. Or go directly to Kaggle and learn "the hard way".

Is there a "The Hundred-Page" statistics book that you can recommend for someone who wants to brush up statistics?

I liked "Head First Statistics" at the time. But may be something better came out, I'm not sure.

Did you write and train neural net to write the book for you?

Haha, I wish I could, but that's technically impossible using the modern level of the technology.

Hi Andriy.

What would be your advice for a Business Intelligence Analyst looking to move on into Data Science?

I would say "forget everything they taught you in school" :-) I know reddit doesn't like self-promotion, but try my book. You can read it entirely before buying here: http://themlbook.com/wiki

I'm involved in development of some industrial hardware. Sometimes we don't know whether a problem is caused by an error in hardware itself (e.g. a configuration resistor missing on the PCB), or hardware is fine but not configured properly (e.g. an FPGA design is not entirely right), or the issue is in CPU software. Could machine learning help us? Or this is wrong kind of a problem to be solved by ML, perhaps because until we find the solution, all our datasets amount to "whatever the input, thing doesn't work"?

I think anomaly detection techniques could help you identify if there's something wrong with the hardware. As for the software, there's whole research domain that tries to analyze the code automatically and spot problems in logic, memory leaks or security. Some try to do it using ML, but it's not my domain, so I don't know how far those solutions can go.

Why is your name spelled Andriy.......and not Andrey?

Because I've got my passport in Ukraine. In this country, they translate Russian names in all official documents. Some translations are not that bad, like mine, but my brother Dmitry has become Dmytro.

Hi! Do you think this book is a good starting point for anyone trying to get closer to this topic?

Of course. This is why I wrote it in the first place.

I understand that not everyone who buys on amazon leaves a review but what i dont understand is how come a book with 5 reviews become a best seller out of occeans of machine learning books? TIA

Because the book has been on Amazon for less than two weeks, it's too early for reviews to start coming in numbers. Why people still buy it if it only has few reviews? I can guess. I think it's the title, it's endorsements from people like Peter Norvig and other respected leaders in the ML/DS space. I hope also that people recommend to one another the book before they even finished reading.

I want to pursue a career in ML, I've got a solid background in computer science, and I've been rigorously going through stats/linear algebra/multivariable calc so I can actually get through an ML book. It's a bit daunting, but I'm determined. Main problem is I don't have a degree, and I feel this will severely limit my ability to get a job. In your experience, how can someone compensate for the lack of a degree in a field where a PhD is the norm?

Oh, PhD was a norm 5-8 years ago. It's no longer the case. However, many hiring managers still look for some diploma in the resume. Try online programs, like Coursera, complete them, get a diploma. It will make your resume more attractive.

Care to share your favorite recipe?

Yes, its called "pâtes aux crevettes sauce rosée". You boil spaghetti. To make a sauce, you cut zucchini into round slices, red bell pepper into dices, and roast it in the vegetable oil. Then you add shrimps, whole mushrooms, tomato sauce and two spoons of sour cream. Add salt and pepper to taste.

You serve spaghettis covered with the sauce with a glass of white wine.

I've not read your book but alphago spurred a deep interest in machine learning, I will look at buying it.

In your opinion, how far away is Machine Learning from being able to write a "The Hundred-Page Machine Learning Book" that is superior to your own and isn't just copied/pasted content? I'd also be interested as to whether it would consider 100 pages too long, too short or just right (obviously that depends on what you consider a successful book).

What you talk about is human-level AI, otherwise called Artificial General Intelligence. It is known to *always* be in 20 years: https://intelligence.org/files/PredictingAI.pdf

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The problem is that we don't know how to build it. If we knew, it would not take much time.

How much is it to self publish on Amazon?

For paperback, Amazon keeps 40% and subtract from your 60% the printing cost. For high quality color print like in my book, the printing cost is $12.

For Kindle, amazon keeps 30% and the author 70% is the book's list price is below $10. Otherwise, Amazon keeps 70% and the author 30%. Amazon also subtract the "digital delivery cost", which for my book is about $4.

Damn, that is expensive. Thank you for the transparency! I suppose we will not be seeing your book in thebookdepository anytime soon then?

Can you explain what thebookdepository is and how it's different from other online bookstores? Wikipedia doesn't provide much explanation.

As far as I know, it's just a store. I usually buy from there because they have free worldwide shipping and I have had only good experiences with it. But since you published with amazon, I am not sure you could sell via other means?

Hardcover will not be published with Amazon, so may be it will get there. I cannot say right now.

If machine learning is something that has been developed in theory for decades but lacked the computing power and data sets to bear fruit, what are we now theorising about, that the computing grunt and data available in the future might make real?

I'm not a futurologist :-) I think that many things from game theory and symbolic logic might find their way into the mainstream.

Hi erm let me start off that I know nothing about Machine Learning BUT my bf is an engineer who recently became interested in Ml and want to walk towards that path later down his career. As someone who knows and does ML, what kind of feedback/comment would you like to hear or to receive from others that pertains to your interest? My bf would show me his remote control robot and I’ll be like cool but I can’t really offer anything else since i dont know anything? Like sure it’s impressive but I want to be more sincere than that :( now I’m thinking of getting your book for him!

Well, I would definitely sound like trying to sell my book, but I'm not. Try my book. The beauty of the thing is that you can read it entirely before buying: http://themlbook.com/wiki

How good did the "read first, buy later" work for you? Would you recommend it as a business model?

It works very well for me and I would definitely recommend it. However, I'm quite sure any publisher would forbid you doing that so you will have to self-publish like me.

Ok so you appear to know an awful lot about AI technology, why are you writing and publishing this book instead of creating the new AlphaGo, or Watson, or some super AI facial recognition criminal catching system, and making billions from it?

Doesn't add up.

So, for you, it's either you are a businessman or a writer?

As someone studying ML at Berkeley right now: I’m noticing that people are increasingly trying to market ML as something that could be more mainstream (i.e., see Google autoML and andrew ng’s new books / startups), and an emphasis on tuning the models over fully understanding the underlying model. Do you think things like it that try and shorten the learning process in favor of technical knowledge, are sufficient for business use cases (non research) over having a graduate level understanding of the models?

I don't believe AutoML will become a thing anytime soon. As for Andrew Ng, he has created a nice Coursera program on neural networks, that explain them in a very good level of detail. The book he wrote should only be used after you have followed this course (or acquired knowledge of neural networks otherwise).

Where am I supposed to read first? I see lots of options online to buy first...

I'd love to read it, but am not ready to buy it.

Here you go: http://themlbook.com/wiki

Do you think your book is enough for a guy to get a junior position in data-science? Everyone and their mother asks for ML knowledge nowadays.

If someone on the interview, can have an intelligent conversation about all the matter that I put in the book, I will definitely hire that person as a Junior.

One other question. What is your favorite forum for discussing ML topics?

I have quite a large number of followers online. Many of them specialists in ML/data science. So I just post online the question I want to discuss and then we discuss.

Any discounts for the book?

I give discounts to people who buy three or more copies of the book. And also to students upon request. For this AMA, here's the link to 10 discounted soft books: https://leanpub.com/theMLbook/c/KNg2sw2aCeIt.

I tried this link but didn't work...

It's because they are all sold :-(

I really want to buy this book. Can you provide an alternate link? The link provided in this post gives me: "Status Code: 422 Unprocessable Entity" when I click the "add ebook to cart" button.

Please PM me.

First of all, thanks for bringing forth this amazing book. It has enjoyable reading through your drafts for individual chapters. Secondly, regarding the discount for students, is it for the soft copy of the physical one? Also, how do I get in touch with you if I want to request the same?

Thank you!

You can find more detail and my contact address on http://themlbook.com.

Thanks for doing the AMA - love your book. Already purchased - my question is around explaining ML/AI concepts to folks. Every time I try to explain any of these concepts, I try and link each of them to something they can relate to in their everyday life. For example, I try and link a neural network to the Google Photos app. Most people have used or experience the Google Photos apps and searched for people/places/things in the app and I try to use that as an example to explain neural networks.

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My question is can you explore that as an option for the next update to your book? Link each of the ML concepts to a publicly available web or mobile app to help make the concept concrete for the user. Thanks

Actually, I hoped that the new edition of TH-PMLB will be exactly 100 pages :-) But as I explained in another answer, for another, unrelated book I think about describing the most important algorithms that shape our culture and lifestyle. In this case, I will definitely give real-life examples.

Hi Andriy, I'm interested in the overlap between machine learning and control theory for dynamic system control. Do you know of any good resources that deal with machine learning from that perspective?

Thanks!

I think reinforcement learning should be applicable in this case. Try "Introduction to Reinforcement Learning" by Sutton and Barto.

Hi Andriy. Are you available for work? Want to share what I’m doing and see maybe there is an opportunity for us to work together. What’s the best way for us to connect?

Hi! Have a full-time job, so I don't look for an additional work. But PM me your idea, may be I can help with an advice.

The books cost so much. Do you have coupon or something for the students? I am still in college and would love to give this book a try.

Did you look at Leanpub.com?

Your book has close to no code. For whom is your book?

Well, my book almost has no code, because it explains almost everything using math and illustrations. In theory (this is what I tried to accomplish) you can read the book, find the additional material on the wiki, if needed, and implement the algorithm based on your understanding. However, today you can google almost everything, including the code, so why put in in the book?

By the way, most illustrations in the book were generated automatically using Python scripts from data. Those python scripts are available on the book's Github.

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I live in Canada. You can't become rich in this country :-)