[HN Gopher] Ask HN: What are the foundational texts for learning... ___________________________________________________________________ Ask HN: What are the foundational texts for learning about AI/ML/NN? I've picked up the following, just wondering what everyone's thoughts are on the best books for a strong foundation: Pattern Recognition and Machine Learning - Bishop Deep Learning - Goodfellow, Bengio, Courville Neural Smithing - Reed, Marks Neural Networks - Haykin Artificial Intelligence - Haugeland Author : mfrieswyk Score : 205 points Date : 2023-01-09 16:34 UTC (6 hours ago) | robg wrote: | Coming from cognitive neuroscience surprised that _Explorations | in Parallel Distributed Processing_ by McClelland and Rumelhart | doesn't get more attention as a classic in bridging old school AI | approaches with the modern paradigm. | | https://psycnet.apa.org/record/1988-97441-000 | junkerm wrote: | In read parts of Murphys "Probabilistic Maschine Laearning" (vol | 1) which is an update of an existing book in ML. It covers a | broad range of topics also very recent developments. It also | includes foundation topics such as probability, linear algebra, | optimization. Also it is quite aligned with the Goodfellow book. | I found it quite challenging at certain points. What helped a lot | was to read a book on bayesian statistics. I used Think Bayes by | Allen Downey for that | (http://allendowney.github.io/ThinkBayes2/index.html) | zffr wrote: | You may also want to consider reading through some of the | important (or highly cited) academic papers in AI/ML/NN. From | these papers you may get a sense of the techniques researchers | are using, and which topics are most important to learn. | | I have not applied this technique to AI/ML/NN specifically, but | it has been useful for me when trying to learn other topics. | raz32dust wrote: | I personally consider Linear algebra to be foundational in AI/ML. | Intro to Linear algebra, Gilbert Strang. And his free course on | MIT OCW is fantastic too. | | While having strong mathematical foundation is useful, I think | developing intuition is even more important. For this, I | recommend Andrew Ng's coursera courses first before you dive too | deep. | mfrieswyk wrote: | I never took beyond Precalculus in school, thanks for the tip! | p1esk wrote: | Oh, most recommendations here assume stem college math | knowledge. You should become comfortable with calculus, | linear algebra, and probability/stats - those are the | foundations of ML. | NationalPark wrote: | Many of the suggestions so far are assuming you have taken | undergraduate linear algebra and calculus. I'd start with | those two subjects, you really can't build a foundational | understanding of modern AI techniques without them. | mythhouse wrote: | i did linear algebra and calculus using strang and spivak | textbooks. Those were classes i enjoy the most. But most of | that stuff has atrophied from my brain over the years, do | you recommend redoing those courses fast or can i learn | when i need it on demand basis. | viscanti wrote: | You can try a refresher on Jacobians. If you're following | everything there well enough, you probably have what you | need to move forward (and pick up the rusty parts that | you need as you go). If you're completely lost then you | probably want to go back for a quick refresher. | jimbokun wrote: | Review on an on demand basis. | | The main concepts are matrix multiplication and | derivatives and their significance. Then you can dig into | the specifics and review or expand your knowledge as | needed. | viscanti wrote: | Strang is great but he covers a lot of things that don't have | much carryover to AI/ML and doesn't really cover things like | Jacobians which do. Maybe there's something more useful for | someone who is only learning Calculus and Linear Algebra for | AI/ML than what Strang teaches. | mindcrime wrote: | Another interesting resource for Linear Algebra is the "Coding | the Matrix" course. | | http://codingthematrix.com/ | | https://www.youtube.com/playlist?list=PLEhMEyM9jSinRHXJgRCOL... | pkoird wrote: | AIMA by Russel and Norvig is a must read IMO. | dmarcos wrote: | I remember Carmack mentioning in a podcast a list of seminal | papers that Ilya Sutskever (@ilyasut) gave to him to learn AI | foundations. I would love to see that list. | davidhunter wrote: | The Quest for Artificial Intelligence: A History of Ideas and | Achievements Nils J. Nilsson | | This is a good overview of the history of the field (up to SVMs | and before deep NNs). I found this useful for putting all the | different approaches into context. | softwaredoug wrote: | "Introduction to Statistical Learning" - | https://www.statlearning.com/ | | (there's also "Elements of Statistical Learning" which is a more | advanced version) | | AI: A Modern Approach - https://aima.cs.berkeley.edu/ | kevinskii wrote: | I agree. I read the first edition to Intro to Statistical | Learning and it went into just the right level of mathematical | depth. The authors also have Youtube lectures that accompany | the chapters, and these are a great reinforcement of the | material. | rg111 wrote: | ISL is a legit good book. Has the correct amount and balance or | rigor and application. | | The explanation, examples, projects, math- all are crisp. | | As the name suggests, it is only an introduction (unlike CLRS). | And it does serve as a great beginners' book giving you proper | foundation for the things that you learn and apply in the | future. | | One thing people complain about is it being written in R, but | no serious hacker should fear R, as it can be picked up in 30 | minutes, and you can implement the ideas in Python. | | As someone with industry experience in Deep Learning, I will | recommend this book. | | The ML course by Andrew Ng has no parallel, though. One must | try and do that course. Not sure about the current iteration, | but the classic one (w/ Octabe/MATLAB) was really great. | bjornsing wrote: | The Elements of Statistical Learning, by Jerome H. Friedman, | Robert Tibshirani, and Trevor Hastie. I've seen it referenced | quite a few times and the TOC looks good. | jtmcmc wrote: | This was one of the first books my advisor told me to read | when I started my ML phd a...long time ago. The fundamentals | of machine learning haven't changed and it's a great book. | master_yoda_1 wrote: | This book is all you need https://probml.github.io/pml- | book/book1.html | stevenbedrick wrote: | To add to the great recommendations on this thread, I really like | Moritz Hardt and Benjamin Recht's "Patterns, Predictions, and | Actions". It's published by Princeton University Press here: | https://press.princeton.edu/books/hardcover/9780691233734/pa... | | But is also available online as a preprint here: | https://mlstory.org/ | 5cott0 wrote: | https://www.manning.com/books/deep-learning-with-python-seco... | digitalsushi wrote: | Are there obvious paths into these spaces for someone stuck over | in devops/infrastructure/platform engineering? Or is it too far a | hop to really find a direct path in? | | Let me ask a slightly different way - can someone like me get | into a job like these, without needing some more college? | | My day job is wrapping up OS templates for people with ML | software and I always wonder what they get to go do with them | once they turn into a compute instance. | jtmcmc wrote: | if you're already doing a job at a company that does this | stuff, can you talk to people about wanting to change teams and | learn? | friendlyHornet wrote: | I would like to know this, as well. | zmgsabst wrote: | Why not ask them? | | Call it cross functional training to increase your domain | knowledge, tell your manager you need it to ensure you're | providing the best service possible, and get your coworkers to | help you learn the framework they use...? | ipnon wrote: | I'd posit we don't understand AIML enough to know their | foundations with much certainty. Take for example the discovery | of emergent zero-shot properties in the latest LLMs. My | recommendation to a beginner would be to grok gradient descent, | matrix multiplication, and the universal approximation theorem, | then get on to engineering like the rest of us. You can't go | wrong with Jeremy Howard's FastAI course and his "Deep Learning | for Coders." | dceddia wrote: | I'm a big fan of learning through practice vs learning all the | theory up front, and for anyone else who feels the same, the Fast | AI course and book are very good: https://fast.ai | | The authors are working on a new course that'll dive deep into | the modern Stable Diffusion stuff too, which I'm looking forward | to. | rg111 wrote: | Do you have Linear Algebra knowledge, and Stats 101 knowledge? | | Then start with ISLR. | | Then go and watch Andrew Ng Machine Learning course on Coursera | (a new version was added in 2022 that uses Python). | | Then read the sklearn book from its maintainers/core devs. It's | from O'Reilly. | | Then go do the Deep Learning Specialization from deeplearning.ai. | | Then do fast.ai course. | | If interested in Deep RL, watch David Silver lectures, then read | Deep RL in Action by Zai, Brown. Then do the HF course on Deep | RL. | | This is how you get started. Choose your books based on your | personality, needs, and contents covered. | | And among MOOCs, I highly suggest the one by Canziani, LeCun from | NYU. (I loved the 2020 version.) | | The one taught by Fei Fei Li and Andrej Karpathy is nice. | | These two MOOCs can substitute classic books based on quality. | | I have never read cover to cover any of the famous books. I read | a lot from them sticking to specific subjects. | | Get to reading papers, finding implementations. Ng + ISLR will | give you good grounds. Fast.ai + deeplearning.ai will give you | capability to solve real problems. NYU + Tubingen + Stanford + | UMich (Justin Johnson) courses will bring you to the edge. | | You need a lot of practical experience that aren't taught | anywhere. So, get your hands dirty early. Learn to use | frameworks, cloud platforms, etc. | | Then start reading papers. | | A crystal clear grasp on Math foundations is a must. Get it if | you don't have already. | TaupeRanger wrote: | There are none anymore. We now know that throwing a bunch of bits | into the linear algebra meat grinder gets you endless high | quality art and decent linguistic functionality. The architecture | of these systems takes maybe a week to deeply understand, or | maybe a month for a beginner. That's really it. Everything else | is obsolete or no longer applicable unless you're interested in | theoretical research on alternatives to the current paradigm. | jtmcmc wrote: | This is definitely a take that ignores the massive amount of | utility for ML that exists outside of generative images and NLP | on the one hand and on the other vastly misrepresents the time | it takes to understand a model, assuming one does not already | have a background in CS, linear algebra and in particular | matrix calculus, probability, stats, etc... | rg111 wrote: | You are plain exaggerating. You can't do all of them in a few | weeks. Algorithms: Lin Reg -> Log Reg -> NN -> CNN + RNN -> | GANs + Transformers -> ViT -> Multimodal AI + LLMs + Diffusion | + Auto Encoders SVM, PCA, kNN, k-means | clustering, etc. LightGBM, XGboost, Catboost, etc. | Optimization and optimizers. Application-wise: | Classification, Semantic Segmentation, Pose Estimation, Text | Generation, Summarization, NER, Image Generation, Captioning, | Sequence Generation (like music/speech), text to speech, speech | to text, recommender systems, sentiment amalysis, tabular data, | etc. Frameworks: pandas, sklearn, PyTorch, | Jax -> training inference, data loading | Platforms: AWS + GCP + Azure And a lot of GPU | shenanigans + framework/platform specific quirks | | All these will take you ~2 years or 1.5 years at least, | | _given that:_ | | - you already know Python/any programming language properly | | - you already know college level math (many people say you | don't need it, but _haven 't met a single soul_ in ML | research/modelling without college level math) | | - you know Stats 101 matching a good uni curriculum and ability | to learn beyond | | - you know git, docker, cli, etc. | | Every influencer and their mother promising to teach you Data | Science in 30 days are plain lying. | | Edit: I see that I left out Deep RL. Let's keep it that way for | now. | | Edit2: Added tree based methods. These are very important. | XGBoost outperforms NNs _every time_ on tabular data. I also | once used an RF head appended to a DNN, for final prediction. | Added optimizers. | jimbokun wrote: | > SVM, PCA, kNN, k-means clustering | | Are these still relevant in the age of Deep Neural Networks? | PeterisP wrote: | Yes, there are all kinds of tasks where the appropriate | solution is to use a DNN for much of the learning (either | directly learning the correlations or as transfer learning | from some large-data self-supervised task) and then, once | you have the results of that DNN inference, work with these | methods - apply PCA for interpreting the resulting vector, | or to separate out specific dimensions to expose them for | adjustment in some generative task; or perhaps the best way | for the final decision is a kNN on top of the DNN output, | etc. | popinman322 wrote: | PCA is a foundational dimension reduction technique, and | kNN can be used in conjunction with embeddings. | | k-means is still great when you have prior/domain knowledge | about the number of groups. | jeffreyrogers wrote: | It's not in your list but decision trees still outperform | DNN on many tabular problems and can be trained faster. | rg111 wrote: | Yes. | | Different problems require different solutions. | | Sometimes, an NN would be overkill. | | And stakeholders in many situations would like insights why | the prediction is what it is. NNs are miles behind LogReg | in terms of interpretablity. | cyber_kinetist wrote: | You still need to understand some basic theory/math about | probabilistic inference (along with some knowledge of linear | algebra), or else you'll get a bit overwhelmed by some of the | equations and not understand what the papers are talking about. | PRML by Bishop is probably more than enough to start reading ML | papers comfortably though. (This would probably be too easy for | a competent math major, but not all of us are trained that way | from the beginning...) | jeffreyrogers wrote: | I'm not sure why you're getting downvoted. I find it hard to | believe that someone without a decently strong math | background could make sense of a modern paper on deep | learning. I have a math minor from a good school and had to | brush up on some topics before papers started making sense to | me. | moneywoes wrote: | What resources are there to understand in a month? | sillysaurusx wrote: | A month to deeply understand? | | I've been doing it since early 2019 and there are still | subtleties that catch me off guard. Get back to me when you're | not surprised that you can get rid of biases from many layers | without harming training. | | I broadly agree with you, but the timeline was just a little | too aggressive. By about 10x. :) | topspin wrote: | > I've been doing it since early 2019 and there are still | subtleties that catch me off guard. | | That's true of every non-trivial discipline. I often learn | subtleties about programming languages and hobbies I've been | dealing with for decades. | hooande wrote: | This is separate from understanding how a language model or | transformer works. You could read the major papers behind | those ideas and read every line of code involved several | times over in a month. I'd recommend it, if you're super | curious. | | You can figure out the bias thing after about a month (or so) | of hands on practice. Do one Kaggle seriously and it'll | become pretty clear, pretty quickly. | ly3xqhl8g9 wrote: | Not sure if foundational (quite a tall order in such a fast- | moving field), but for sure a nice introduction into neural | networks, and even mathematics in general (for a teenager, | because it's nice to see numbers in action beyond school-level | algebra): | | - Harrison Kinsley, Daniel Kukiela, _Neural Networks from | Scratch_ , https://nnfs.io, | https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0Qu... | | Somewhat foundational, if not in actuality, then in the intention | to actually build a theory as in theory of gravitation, although | not necessarily an introductory text: | | - Daniel A. Roberts, Sho Yaida, _The Principles of Deep Learning | Theory_ , https://arxiv.org/abs/2106.10165 | bilsbie wrote: | If anyone is just starting and out wanting to do a study group | let me know. | | I'm having trouble keeping my motivation up but I really want to | get up to speed on how LLM's work and someday make a career | switch. | moneywoes wrote: | Im down | adg001 wrote: | I have not seen mentioned so far in this thread the following | book, which I can't recommend more highly: | | Understanding Machine Learning: From Theory To Algorithms - Shai | Shalev-Shwartz | dezzeus wrote: | You may want to also consider this one: | | Artificial Intelligence, a modern approach - Stuart Russell, | Peter Norvig | apu wrote: | The big book of stuff that doesn't work. | rzzzt wrote: | Prop it up with a small stick and put some cracked walnuts | below to catch mice with it. | mindcrime wrote: | Can't recommend this highly enough, if for no other reason than | to provide some context to help the OP from getting trapped in | the "deep learning is all you need" echo-chamber. Sure ANN's | and DL are great and do amazing things, but until it's proven | that they really are the "be all, end all" (something I suspect | we're far from) then it makes sense to dedicate at least _some_ | cycles to considering other paradigms. | bjornsing wrote: | It's probably a bit off the beaten path, but I can highly | recommend Probability Theory, The Logic of Science, by E. T. | Jaynes. | | In the opening chapter Jaynes describes a hypothetical system he | calls "The Robot". He then lays out the mathematics of the "The | Robot's" thinking in detail: essentially Bayesian probability | theory. This is the best summary of an ideal ML/AI system I've | come across. It's also very philosophically enlightening. | misiti3780 wrote: | seconded! it's a great book. | sillysaurusx wrote: | I'm so sad the editor chose not to publish Jaynes' C snippets | because "they were too cryptic." They would've helped clarify | the ideas greatly. | | It's a good book, but I don't know how it's related to ML. My | own answer would be "Just do it." Find an ML project you like | and start tinkering around. But everyone learns differently, so | maybe there's a book that can replace experience. | bjornsing wrote: | How is Jaynes (2003) related to ML? I guess in the same way | probability theory is related to ML: it underpins just about | every meaningful step forward in ML/AI research, as I see it. | IanCal wrote: | I think a good start is to think about what you want to do. "Back | in my day" ai was mostly academic and had more classic | foundational parts with newer flashy bits. It wasn't, broadly, | applicable to the real world. Some parts but not a huge amount. | | Now I think you've got key parts. There's how to _use_ recent | production ready models /systems, how to _train_ them and how to | _make_ them. Is it in a research or business context? | | The field is also broad enough that any one section (text, | images, probably symbols) and subsection (time series, bulk, fast | online work) all have significant bodies of work behind them. My | splits here will not be the best currently so I'm happy for any | corrections on a useful hierarchy by the way. | | Perhaps you're interested in the history and what's led up to | today's work? That's more of a "brief history of time" style | coverage, but illuminating. | | I'm aware I've not helpfully answered, but I think the same | question could have very different valid goals and wanted to | bring that to the fore. | alphabetting wrote: | For a less technical history of the field and major players I'd | recommend Genius Makers. | crosen99 wrote: | "Neural Networks and Deep Learning", by Michael Nielsen | http://neuralnetworksanddeeplearning.com (full text) | | The first chapter walks through a neural network that recognizes | handwritten digits implemented in a little over 70 lines of | Python and leaves you with a very satisfying basic understanding | of how neural networks operate and how they are trained. | martythemaniak wrote: | This is the thing that made NNs "click" for me, I think it was | very good. Before this I did Andrew Ng's old ML course on | coursera, so I thought that was a good intro to old ML | approaches, common terms/techniques and flowed nicely into NNs. | | But there's are both kinda old now, so there must be something | newer that'll give you an equally good intro to transformers, | etc. | gaspb wrote: | If you're more inclined to theory, I would suggest "Learning | Theory from First Principles" by F. Bach: | https://www.di.ens.fr/~fbach/ltfp_book.pdf | | The book assumes limited knowledge (similar to what is required | for Pattern Recognition I would say) and gives a good intuition | on foundational principles of machine learning (bias/variance | tradeoff) before delving to more recent research problems. Part I | is great if you simply want to know what are the core tenets of | learning theory! | PartiallyTyped wrote: | I recommend against DL by Goodfellow. At this point it is pretty | much outdated. Actually, anything specific to NNs is already | outdated by release. | | You'd need the following background: | | - Linear Algebra | | - Multivariate Calculus | | - Probability theory && Statistics | | Then you need a decent ML book to get the foundations of ML, you | can't go wrong with either of these: | | - Bishop's Pattern Recognition | | - Murphy's Probabilistic ML | | - Elements of statistical learning | | - Learning from data | | You can supplement Murphy's with the advanced book. Elements is a | pretty tough book, consider going through "Introduction to | statistical learning"[1]. Bishop and Murphy include foundational | topics in mathematics. | | LfD is a great introductory book and covers one of the most | important aspects of ML, that is, model complexity and families | of models. It can be supplemented with any of the other books. | | I'd also recommend doing some abstract algebra, but it's not a | prerequisite. | | If you would like a top-down approach, I recommend getting the | book "Mathematics of Machine Learning" and learning as needed. | | For NN methods, some recommendations: | | - https://paperswithcode.com/methods/category/regularization | | - https://paperswithcode.com/methods/category/stochastic-optim... | | - https://paperswithcode.com/methods/category/attention-mechan... | | - https://paperswithcode.com/paper/auto-encoding-variational-b... | | For something a little bit different but worth reading given that | you have the prerequisite mathematical maturity | | - Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and | Gauges | https://arxiv.org/abs/2104.13478 | | [1] https://www.statlearning.com/ | | Many thanks to the user "mindcrime" for catching my error with | Introduction to statistical learning. | mindcrime wrote: | _consider going through "Introductions to Elements of | statistical learning"_ | | Was that supposed to be _An Introduction to Statistical | Learning_ [1] or maybe _Introduction to Statistical Relational | Learning_ [2]? I don't think there is a book titled | _Introduction to Elements of Statistical Learning_? | | [1]: https://www.statlearning.com/ | | [2]: https://www.cs.umd.edu/srl-book/ | PartiallyTyped wrote: | I referred to [1], thanks I have corrected GP. | sillysaurusx wrote: | (I can't wait until the myth that you need linear algebra and | calculus to do ML finally dies. It's like saying that you need | to understand assembly to do programming. It helps, but it's | far from a requirement.) | 6gvONxR4sf7o wrote: | I disagree strongly. In your analogy, if the compiler broke | down all the time, you would probably need to understand | assembly to do programming. ML is amazing today, but still | kinda sucks. In general you'll have a bunch of failures on | the way to a successful novel application, so it's more | critical to understand what's going on under the hood in ML | than in your programming analogy. | | If you just want to apply well known things to well known | things, sure you're right. But as soon as things go wrong, I | couldn't imagine how much more inefficient my iteration | cycles would be trying to do novel work without understanding | linear algebra (for some kinds of novel work) or calc (for | other kinds of novel work). I think you kinda get at this | when you say it's not necessary but it helps. It's not | necessary, but it helps _a lot_ with anything off the beaten | track. | sillysaurusx wrote: | We agree, I think! | | And certainly, if you're one of those people who can pull | it off, studying ML from first principles is probably an | advantage. I just wince every time since I wouldn't have | gotten into ML in the first place if I had to start with a | big Calculus tome. There are probably a lot of people like | me out there. | PartiallyTyped wrote: | OP asked for foundational, and I provided _foundational_. | In my opinion, everyone should start from some sound | foundations in LinAlg and Calculus. | | Here are a couple of errors that stem from a single | foundational problem: | | - a linear regressor can not be more than the number of | datapoints | | - dimensionality reduction when you have NxM with M > N | is bogus and you need a bigger dataset to do anything | meaningful other than clustering | | - input dimension of output layer is larger than the | number of samples | | The underlying issue in all of these is the rank nullity | theorem which is pretty foundational for ML, and yet many | practitioners don't know about it or haven't made the | connection. | | I am not expressing that you should have gone through | Spivak or build bottom up. There are books like | mathematics of ML that condense everything you need, | giving you a decent enough foundation for what you will | need. | antegamisou wrote: | > I can't wait until the myth that you need linear algebra | and calculus to do ML finally dies. | | This is such a dangerously absurd claim.. but then, it speaks | volumes about the abysmal state the non-research heavy AI/ML | field has fallen into. | antegamisou wrote: | As always on HN, the right answer is at the bottom. | KRAKRISMOTT wrote: | Haugeland is GOFAI/cognitive science, not directly relevant to | modern machine learning variety of models unless you are doing | reinforcement learning or trees stuff (hey poker/chess/Go bots | are pretty cool!). Russel and Norvig are the typical introductory | textbooks for those. Marks and Haykins are all severely out of | date (they have solid content, but they don't have the same | _scale_ of modern deep learning which has many emergent | properties). | | You are approaching this like an established natural sciences | field where old classics = good. This is not true for ML. ML is | developing and evolving quickly. | | I suggest taking a look at Kevin Murphy's series for the | foundational knowledge. Sutton and Barto for reinforcement | learning. Mackay's learning algorithms and information theory | book is also excellent. | | Kochenderfer's ML series is also excellent if you like control | theory and cybernetics | | https://algorithmsbook.com/ | https://mitpress.mit.edu/9780262039420/algorithms-for-optimi... | https://mitpress.mit.edu/9780262029254/decision-making-under... | | For applied deep learning texts beyond the basics, I recommend | picking up some books/review papers on LLMs, Transformers, GANs. | For classic NLP, Jurafsky is the go-to. | | Seminal deep learning papers: | https://github.com/anubhavshrimal/Machine-Learning-Research-... | | Data engineering/science: https://github.com/eugeneyan/applied-ml | | For speculation: https://en.m.wikipedia.org/wiki/Possible_Minds | ipnon wrote: | To your second point I have a sneaking suspicion whatever is | recommended in this very thread will suddenly jump in its | estimation as a "classic." History is made up as it goes along! | KRAKRISMOTT wrote: | Well, GP's _Neural Smithing_ is a solid example. There is | nothing wrong with it, it is surprisingly well written and | correct for something published before the millenium. | | https://books.google.com/books/about/Neural_Smithing.html?id. | .. | | Take a look at the Google Books preview (click view sample). | The basics are all there, intro to biological history of | neural networks, backpropagation, gradient descent, and | partial derivatives etc. It even hints at teacher-student | methods! | | The only issue is that it missed out on two decades of | hardware development (and a bag of other optimization | tricks). Modern deep learning implementations requires | machine sympathy at scale. It also doesn't have any | literature on autoregressive networks like RNNs or image | processing tricks like CNNs. | mfrieswyk wrote: | Appreciate the comment very much. I feel like I need to build a | foundation context in order to appreciate the significance of | the latest developments, but I agree that most of what I posted | doesn't represent the state of the art. | starwind wrote: | Does the order matter for Kochenderfer? Any one of those put | more emphasis on controls than the others? | mtlmtlmtlmtl wrote: | A quick point about the "tree stuff" and Norvig&Russell: | | While it does cover minimax trees, alphabeta etc, it only | really provides a very brief overview. The book is more of an | overview of the AI/ML fields as a whole. Game playing AI is | dense with various game-specific heuristics that the book | scarcely mentions. | | Not sure about books, but the best resource I've found on at | least chess AI is chessprogramming.org, then just ingesting the | papers from the field. | cscurmudgeon wrote: | Get a strong grasp on Linear Algebra and everything else falls | into place more easily | | https://math.mit.edu/~gs/learningfromdata/ | gerash wrote: | I'd suggest these two by Kevin Murphy: | | Probabilistic Machine Learning: An Introduction | | https://probml.github.io/pml-book/book1.html | | Probabilistic Machine Learning: Advanced Topics | | https://probml.github.io/pml-book/book2.html | pablo24602 wrote: | Working through these right now- definitely recommend them | 6gvONxR4sf7o wrote: | Kevin Murphy's books (especially the new ones) are what I'd point | anyone towards for ML. | epgui wrote: | The foundations of AI/ML are really linear algebra and | statistics. But not the kinds of stats most people learn in | undergrad: focus on linear models (there are tons of great books | on just that; also look up "common statistical tests are linear | models" for a great intro into what i'd call useful stats), | bayesian stats, anova/manova/permanova, etc. | avipeltz wrote: | - _AIMA by Russel and Norvig_ is a classic but I would say is | more of overview of the field and for most topic areas isn 't | quite deep enough imo. | | - For deep learning specifically, a more applied text that is | beautifully written and chock full of examples is Francois | Chollet's _Deep Learning with Python_ (there a new second edition | out with up to date examples using modern versions of | Tensorflow). The first 3 chapters I would give as required | reading for anyone interested in understanding some deep learning | fundamentals. | | - _Deep Learning - goodfellow and bengio_ - seems like it would | be hard to get through without a reading group not exactly a APUE | or K &R type reading experience but I haven't spent enough time | with it. | | If you haven't taken a Linear Algebra or Differential Equations | class its useful stuff to know for ML/DL theory but not fully | necessary to do applied work with modern high level libraries, | but definitely having a strong understanding of basic matrix math | is useful. | | If you have interests in natural language processing theres a | couple good books: | | - _Natural Language Processing with Python - Bird Klein, Loper_ , | is a great intro to NLP concepts and working with NLTK which may | be a bit dated to some but I would definitely recommend, and its | online for free. Great examples.(https://www.nltk.org/book/) | | - _Speech and Language Processing - Dan Jurafsky and James H. | Martin_ - is good, though I have only spent much time with the | pre-print | | And then theres a lot of papers that are good reads. Let me know | if you have any questions or want a list of good papers. | | If you just want to get off the ground and start playing with | stuff and building things I'd recommend fast.ai's free online | course - its pretty high level and a lot is abstracted away but | its a great start and can enable you to build lots of cool things | pretty rapidly. Andrew Ng's online course also is quite | requitable and will probably give you a bit more background and | fundamentals. | | If I were to choose one book from the bunch it would be Chollet | it gives you pretty much all the building blocks you need to be | able to read some papers and try to implement things yourself and | I find building things a much more satisfying way to learn than | sitting down and writing proofs or just taking notes but thats | just my preference. | rg111 wrote: | Norvig-Russel has many chapters spanning hundreds of pages that | are way out of date and not used anywhere. | | And the new things he cover are covered in a better manner and | better depth in other sources. | | I read this book like a novel. Good for a basic overview, but | the RoI is very low. | daturkel wrote: | I maintain a list of well-known or foundational papers in ML in a | github repo that may be of interest to readers of this thread | | https://github.com/daturkel/learning-papers | bradreaves2 wrote: | This is off the beaten path, but consider Abu-Mostafa et al.'s | "Learning from Data". https://www.amazon.com/Learning-Data-Yaser- | S-Abu-Mostafa/dp/... | | I adore PRML, but the scope and depth is overwhelming. LfD | encapsulates a number of really core principles in a simple text. | The companion course is outstanding and available on EdX. | | The tradeoff is that LfD doesn't cover a lot of breath in terms | of looking at specific algorithms, but your other texts will do a | better job there. | | My second recommendation is to read the documentation for | Scikit.Learn. It's amazingly instructive and a practical guide to | doing ML in practice. | PartiallyTyped wrote: | LfD is a great book to get people to think about complexity | classes and model families. We used that in my grad course and | I can recommend it. | vowelless wrote: | I strongly second this. Abu Mostafa has videos and homework for | this course too. This course was the one that made a LOT of | fundamental things "click", like, why does learning even work | and what are some broad expectations about what we can and | cannot learn. ___________________________________________________________________ (page generated 2023-01-09 23:01 UTC)