Reddit reviews Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
We found 26 Reddit comments about Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series). Here are the top ones, ranked by their Reddit score.
Mit Press
Reading some books would be a good idea.
The following are textbooks:
General AI
Machine Learning
Statistics for Machine Learning
There are many other topics within AI which none of these books focus on, such as Natural Language Processing, Computer Vision, AI Alignment/Control/Ethics, and Philosophy of AI. libgen.io may be of great help to you.
Having done an MEng at Oxford where I dabbled in ML, the 3 key texts that came up as references in a lot of lectures were these:
Pattern Recognition and Machine Learning (Information Science and Statistics) (Information Science and Statistics) https://www.amazon.co.uk/dp/0387310738/ref=cm_sw_r_cp_apa_i_TZGnDb24TFV9M
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series) https://www.amazon.co.uk/dp/0262018020/ref=cm_sw_r_cp_apa_i_g1GnDb5VTRRP9
(Pretty sure Murphy was one of our lecturers actually?)
Bayesian Reasoning and Machine Learning https://www.amazon.co.uk/dp/0521518148/ref=cm_sw_r_cp_apa_i_81GnDbV7YQ2WJ
There were ofc others, and plenty of other sources and references too, but you can't go buying dozens of text books, not least cuz they would repeat the same things.
If you need some general maths reading too then pretty much all the useful (non specialist) maths we used for 4 years is all in this:
Advanced Engineering Mathematics https://www.amazon.co.uk/dp/0470646136/ref=cm_sw_r_cp_apa_i_B5GnDbNST8HZR
If you're having to ask this, it means you haven't read enough textbooks for reading papers to make sense.
What I mean is that to make sense of most research papers you need to have a certain level of familiarity with the field, and the best way to achieve that familiarity is by reading textbooks. Thing is, if you read those textbooks you'll acquire a familiarity with the field that'll let you identify which papers you should focus on studying.
Now go read MLAPP cover to cover.
Try going through the Kevin Murphy book : https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020
Don't worry, you've demonstrated the ability to figure out whatever you need to get hired, you need to worry more about getting a place to live. probably you shd buy one of those shirts that says "Keep calm and carry on". You could cram on java performance tuning or kernel methods or hadoop or whatever and be handed a project that doesn't use it. Here's some "curricula", free books etc
http://web.archive.org/web/20101102120728/http://measuringmeasures.com/blog/2010/3/12/learning-about-machine-learning-2nd-ed.html
http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science
http://metaoptimize.com/qa/questions/186/good-freely-available-textbooks-on-machine-learning
http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/product-reviews/0262018020/ (first review)
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http://people.seas.harvard.edu/~mgelbart/glossary.html
http://www.quora.com/Machine-Learning
http://www.quora.com/Machine-Learning-Applications
Vezi si fast.ai, au 4 cursuri foarte bune. Apoi si asta e bun. Hugo Larochelle avea un curs de retele neuronale, un pic mai vechi.
La carti as adauga si The Hundred Page Machine Learning Book si asta , probabil cea mai buna carte practica, da asteapta editia a 2a, cu tensorflow 2.0, are tf.keras.layers, sequential model, practic tf 2 include keras si scapi de kkturile alea de sessions. Asa, si ar mai fi si asta, asta si asta. Nu pierde timp cu cartea lui Bengio de deep learning, e o mizerie superficiala. Spor la invatat si sa vedem cat mai multi romani cu articole pe ML si DL!
Take the online course by Andrew Ng and then read Python Machine Learning.
If you then become really serious about Machine Learning, read, in this order,
These books are available in India
http://www.amazon.in/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020
http://www.amazon.in/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_1?s=books&ie=UTF8&qid=1502576905&sr=1-1&keywords=Pattern+Recognition+and+Machine+Learning+by+Christopher+Bishop
http://www.amazon.in/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_fkmr0_1?s=books&ie=UTF8&qid=1502576961&sr=1-1-fkmr0&keywords=Deep+learning+by+Yousha+Bengio
I tried to put a pakistani address to check if amazon india ships there, http://imgur.com/a/OIhIL but it doesn't.
Sorry bro, I wish there was something I could do.
Are you referring to Machine Learning: A Probabilistic Perspective? (link to Amazon)
Bishop's book Pattern Recognition and Machine Learning is pretty great IMHO, and is considered to be the Bible in ML - although, apparently, it is in competition with Murphy's book Machine Learning: A Probabilistic Approach. Murphy's book is also supposed to be a gentler intro. With an ECE background the math shouldn't be too difficult to get into in either of these books. Depending on your background (i.e. if you've done a bunch of information theory) you might also like MacKay's book Information Theory, Inference and Learning Algorithms. MacKay's book has a free digital version and MacKay's 16 part lecture series based on the books is also available online.
While those books are great, I wouldn't actually recommend just reading through them, but rather using them as references when trying to understand something in particular. I think you're better off watching some lectures to get your toes wet before jumping in the deep end with the books. MacKay's lectures (liked with the book) are great. As are Andrew Ng's that @CatZach mentioned. As @CatZach mentioned Deep Learning has had a big impact on CV so if you find that you need to go that route then you might also want to do Ng's DL course, though unlike the courses this one isn't free :(.
Finally, all of the above recommendations (with the exception of Ng's ML course) are pretty theory driven, so if you are more of a practical person, you might like Fast.AI's free deep learning courses which have very little theory but still manage to give a pretty good intuition for why and how things work! You probably don't need to bother with part 2 since it is more advanced stuff (and will be updated soon anyways so I would try wait for that if you do want to do it :))
Good luck! I am also happy to help with more specific questions!
If you just try to eyeball patterns from historical charts, I guarantee you will see it because that's just what the brain has evolved to do: spotting patterns well (e.g. Jesus on a toast), even when it's actually due to random chance. That's also why most of the so-called technical 'analysis' are bullshit.
Instead, approach this in a systematic and principled manner. You can try check out this book to get an idea what I'm talking about: Pattern Recognition and Machine Learning. This is the standard grad-level introduction to the field, but might be rather heavy for some. An easier read is this one. You can find the PDF of these books online through some searching or just head to your local library. Approaching the problem from a probabilistic and statistical angle also lets you know the extent of what you can predict and more importantly, what the limitations are and when the approach breaks down -- which happens a lot actually.
TL;DR: predicting patterns is hard. That's why stats is the sexy new job of the century, alongside with 'data science' (hate that term uuurgh).
Murphy
BRML
ESL
piggybacking on what /u/T4IR-PR said, the best book to attack the science aspect of AI is Artifical Intelligence: A Modern Approach. It was the standard AI textbook when I took the class and it's honestly written very well - people with a basic undergraduate understanding of cs/math can jump right in and start playing with the ideas it presents, and it gives you a really nice outline of some of the big ideas in AI historically. It's one of the few CS textbooks that I recommend people buy the physical copy of.
Note that a lot of the field of AI has been moving more towards ML, so if you're really interested I would look into books regarding that. I don't know what intro texts you would want to use, but I personally have copies of the following texts that I would recommend
and to go w/ that
for some more maths background, if you're a stats/info theory junky.
After all that, if you're more interested in a philosophy/theoretical take on AI then I think Superintelligence is good (I've heard?)
>There have been some excellent trading opportunities with returns as high as 30% to your overall portfolio! Crypto is providing big returns that are uncommon in traditional markets.
I guess you have a good intention, Mr. Hustle, but I'd hate to see the kind shibes here being taken advantage of again. You should be more objective and also warn people that they can as easily lose that much of money when trading, especially when they don't know what they are doing initially.
And the effectiveness of technical 'analysis' is a highly debatable issue. I'd just leave this quote from Wikipedia:
> Technical analysis is widely used among traders and financial professionals and is very often used by active day traders, market makers and pit traders. In the 1960s and 1970s it was widely dismissed by academics. In a recent review, Irwin and Park[13] reported that 56 of 95 modern studies found that it produces positive results but noted that many of the positive results were rendered dubious by issues such as data snooping, so that the evidence in support of technical analysis was inconclusive; it is still considered by many academics to be pseudoscience.[14] Academics such as Eugene Fama say the evidence for technical analysis is sparse and is inconsistent with the weak form of the efficient-market hypothesis.[15][16] Users hold that even if technical analysis cannot predict the future, it helps to identify trading opportunities.[17]
...
> Whether technical analysis actually works is a matter of controversy. Methods vary greatly, and different technical analysts can sometimes make contradictory predictions from the same data. Many investors claim that they experience positive returns, but academic appraisals often find that it has little predictive power.[51] Of 95 modern studies, 56 concluded that technical analysis had positive results, although data-snooping bias and other problems make the analysis difficult.[13] Nonlinear prediction using neural networks occasionally produces statistically significant prediction results.[52] A Federal Reserve working paper[21] regarding support and resistance levels in short-term foreign exchange rates "offers strong evidence that the levels help to predict intraday trend interruptions," although the "predictive power" of those levels was "found to vary across the exchange rates and firms examined".
I'm not saying not to take coaching from DogeHustle, just that if people want to do it, be aware of its 'limitation' too and have fun doing it with your disposable money only. As an alternative, I strongly suggest shibes who want to try predicting the future based on pattern analysis to do it in a principled manner and learn math, stats and machine learning. It won't be easy, but it will have a wide application beyond trading (so-called data 'science' is the hot job nowadays). It will also teach you the limitation of such methods, and when it might fail, especially in such a manipulated market like crypto. This is a good book to start with:
http://www.amazon.co.uk/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020
A good lightweight introduction: Programming Collective Intelligence
If you'd like a single, but more difficult, reference that covers much of the breadth of machine learning: Machine Learning: A probabiliistic Perspective
I head that the newer Machine Learning: A Probabilistic Perspective is equally good, and from the small amount I've read so far I'd agree.
Try to get through the first chapter of Bickel and Doksum. It is a great book on mathematical statistics. You need a solid foundation before you can build up.
For a less rigorous, more applied and broad book, I thought this book was alright. Just realize that the more "heavy math" (i.e., mathematical statistics and probability theory) you do, the better prepared you will be to face applied problems later. A lot of people want to jump right into the applications and the latest and greatest algorithms, but if you go this route, you will never be the one greatly improving such algorithms or coming up with the next one (and you might even run the risk of not fully understanding these tools and when they do not apply).
Na verdade eu sou físico. Acho que é mais comum entre os físicos adotar uma perspectiva bayesiana do que entre os matemáticos ou mesmo os estatísticos. Talvez por causa da influência do Edwin T. Jayes, que era físico. Talvez por causa da conexão com teoria de informação e a tentadora conexão com termodinâmica e mecânica estatística.
O meu interesse pela perspectiva Bayesiana começou por conta do grupo de pesquisa onde fiz o doutorado. Meus orientador e meu co-orientador são fortemente bayesianos, e o irmão do meu orientador de doutorado é um pesquisador bastante conhecido das bases epistemológicas da teoria bayesiana (o físico uruguaio Ariel Caticha).
Tem vários livros bons sobre probabilidade bayesiana, depende muito do seu interesse.
O primeiro livro que eu li sobre o assunto foi justamente o do Jaynes - Probability Theory, the Logic of Science. Esse é um livro um pouco polêmico porque ele adota uma visão epistemológica bastante forte e argumenta de forma bastante agressiva a favor dela.
Uma visão um pouco alternativa, bastante conectada com teoria de informação e também fortemente epistemológica você pode encontrar no livro Lectures on Probability, Entropy and Statistical Physics do Ariel Caticha - (de graça aqui: https://arxiv.org/abs/0808.0012). Eu fui aluno de doutorado do irmão do Ariel, o Nestor Caticha. Ambos têm uma visão bastante fascinante de teoria de probabilidades e teoria da informação e das implicações delas para a física e a ciência em geral.
Esses livros são mais visões epistemológicas e teóricas, e bem menos úteis para aplicação. Se você se interessa por aplicação tem o famoso BDA3 - Bayesian Data Analysis, 3ª edição e também o Doing Bayesian Data Analysis do John Kruschke que tem exemplos em R.
Tem um livrinho bem introdutório também chamado Bayesian Methods for Hackers do Cam-Davidson Pylon (de graça aqui: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) que usa exemplos em python (pymc). É bem basicão para aprender aplicações de probabilidades bayesianas.
O livro All of Statistics do Larry Wasserman tem uma parte introdutória também de inferência bayesiana.
Se você em interesse por inteligência artificial um outro livro muito bacana é o do físico britânico (recentemente falecido) David Mackay - Information Theory, Inference, and Learning Algorithms (de graça aqui: http://www.inference.phy.cam.ac.uk/mackay/itila/). Esse livro foi meu primeiro contato com Aprendizado de Máquina e é bem bacana.
Outros livros bacanas de Aprendizado de Máquina que usam uma perspectiva bayesiana são Bayesian Reasoning and Machine Learning (David Barber) e o livro-texto que tem sido o mais usado para essa área que é o Machine Learning: a Probabilistic Perspective (Kevin Murphy).
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series
I was in your shoes not long ago, though a much diff background and job situation.
> I guess maybe my question boils down to do I need to at some point go to grad school?
Yes but don't worry about grad school right now. It's expensive and you'll do better with it once you've been working in the real world. Try and get work to pay for it too.
>I'm not against it, but would rather learn on my own and make it that way, is that feasible?
Yes you can start using ML techniques at work without formal training. Don't let it stop you. Get a good book - I use Kevin Murphy's and also have a copy of EoSL on my desk from the work library (its free online pdf though).
ML is a somewhat broad and growing field. So if you have the mindset that you need to cover it all before you start using it you'll be sticking thumbs up your ass for a few years.
A better approach will be what is your specific data. Just like you're probably familiar with from using SQL, standard textbook techniques or something in a research paper rarely applies exactly to you what you're working with. So it's almost better to approach your problem directly. Explore the data, look at the data, study the data (in a stats fashion) and then look into what could an intelligent program do to better analyze it. And then in the meantime you can study more general ML topics after work.
I hadn't heard of Lie Groups as well (and didn't look it up the first time you mentioned them) - they sound amazing!
Right now I'm mainly reading the Murphy Book after having finished Probabilistic Models of Cognition (which I enjoyed because I also always wanted to check out Scheme and has some great interactivity).
I suppose I'll have to put these books on the list, thanks! ;)
You should look for highly rated books in the subject you're interested in to get an idea of what you might want to learn. This information will generally be contained either in the preface or introduction chapters. Some books also contain appendices with maths background they think a reader needs. For example in Machine Learning: A Probabilistic Perspective under Preface > Target Audience:
> This book is suitable for upper-level undergraduate students and beginning graduate students in
computer science, statistics, electrical engineering, econometrics, or any one else who has the
appropriate mathematical background. Specifically, the reader is assumed to already be familiar
with basic multivariate calculus, probability, linear algebra, and computer programming. Prior
exposure to statistics is helpful but not necessary.
and in Pattern Recognition and Machine Learning the introduction says
> Knowledge of multivariate calculus and basic linear algebra
is required, and some familiarity with probabilities would be helpful though not essential
as the book includes a self-contained introduction to basic probability theory.
as well as in the appendix
> Appendix A Data Sets 677
>
> Appendix B Probability Distributions 685
>
> Appendix C Properties of Matrices 695
>
> Appendix D Calculus of Variations 703
>
> Appendix E Lagrange Multipliers 707
Classic Russel & Norwig textbook is definitely worth reading. It starts from basics and goes to quite advanced topics:
http://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/
Udacity has AI class that follows some chapters of that book.
Murphy's textbook builds ML from the ground up, starting from basics of probability theory:
http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/
(I see, it was already recommended)
Coursera has the whole machine learning specialization (Python) and a famous ML class by Andrew Ng (Matlab).
I hope it helps. Good luck!
I used two main resources to start in ML:
Both are quite math-heavy but it gave me a very solid basis to start building on. For learning Tensorflow I used examples and the documentation.
Real answer: https://www.amazon.co.uk/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020