Best products from r/MachineLearning
We found 93 comments on r/MachineLearning discussing the most recommended products. We ran sentiment analysis on each of these comments to determine how redditors feel about different products. We found 222 products and ranked them based on the amount of positive reactions they received. Here are the top 20.
1. InFerence (Artificial Intelligence)
- Officially Licensed Sanrio Aggretsuko Apparel
- 8.5 oz, Classic fit, Twill-taped neck
Features:
2. Pattern Recognition and Machine Learning (Information Science and Statistics)
- Springer
Features:
3. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Mit Press
4. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- O Reilly Media
Features:
5. Probability Theory: The Logic of Science
- Used Book in Good Condition
Features:
6. Deep Learning (Adaptive Computation and Machine Learning series)
The MIT Press
7. Programming Collective Intelligence: Building Smart Web 2.0 Applications
- O Reilly Media
Features:
8. Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow
10. Artificial Intelligence: A Modern Approach (3rd Edition)
- Overnight shipping available
Features:
12. Data Smart: Using Data Science to Transform Information into Insight
- Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.
- But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.
Features:
14. Recommender Systems: The Textbook
- Product Name : Network Modular PCB Jack;Model : 5224-8P8C
- Type : 8 Pin RJ45;Pitch : 2mm/ 0.07"
- Soldering Distance : 12mm/ 0.5";Total Size : 15 x 15 x 16cm/ 0.6'' x 0.6'' x 0.62''(L*W*H)
- Material : Plastic, Electronic Parts;Main Color : Black
- Net Weight : 19g;Package Content : 5 x Network Modular PCB Jack
Features:
15. Introduction to Machine Learning (Adaptive Computation and Machine Learning series)
16. SanDisk SSD PLUS 240GB Internal SSD - SATA III 6 Gb/s, 2.5"/7mm, Up to 530 MB/s - SDSSDA-240G-G26
- Easy upgrade for faster boot-up, shutdown, application load and response (As compared to 5400 RPM SATA 2.5” hard drive. Based on published specifications and internal benchmarking tests using PCMark Vantage scores.)
- Boosts burst write performance, making it ideal for typical PC workloads
- The perfect balance of performance and reliability
- Read/write speeds of up to 530MB/s/440MB/s (Based on internal testing; performance may vary depending upon drive capacity, host device, OS and application.)
- Shock-resistant for proven durability —even if you drop your computer (Shock resistant (up to 1500G) and vibration resistant (5gRMS, 10-2000 HZ/4.9 gRMS, 7-800 HZ), Temperature (from 0 degrees Celcius to 70 degrees Celcius))
- Order with your Alexa enabled device. Just ask "Alexa, order SanDisk Internal SSD."
- Compatible devices: Desktop
Features:
18. Foundations of Machine Learning (Adaptive Computation and Machine Learning series)
- ✅Universal Design - Fits standard height toilets which ranging in height from 14" to 16.5" from floor to toilet bowl seat. Does NOT fit high toilets which over 16.5" high, such as Comfort Height and ADA Compliant Height toilets
- ✅Safe and Comfortable - Our potty trainer is made of high quality PP,can hold 75kg(165lb) mostly,sturdy enough when your toddler climbs up/down. padding on the potty seat makes it comfortable
- ✅Adjustable Footrest - Our potty training seat is suitable for 1-8 years old,as the footrest is adjustable(2 choice of height) so you can continue to use it when kids grow up
- ✅Training Toddler - These toilet trainer ladder is both suitable for boys and girls with pee catcher design ,makes her or him super interested in potty time
- ✅Easy to Assemble - Easy to install according our instruction.and our potty toilet seat is suitable for V-shaped, U-shaped, O-shaped toilets, not for square toilets and It is very easy to remove and fold up so adults can use the toilet too
Features:
Imagine you have a dataset without labels, but you want to solve a supervised problem with it, so you're going to try to collect labels. Let's say they are pictures of dogs and cats and you want to create labels to classify them.
One thing you could do is the following process:
(I'm ignoring problems like pictures that are difficult to classify or lazy or adversarial humans giving you noisy labels)
That's one way to do it, but is it the most efficient way? Imagine all your pictures are from only 10 cats and 10 dogs. Suppose they are sorted by individual. When you label the first picture, you get some information about the problem of classifying cats and dogs. When you label another picture of the same cat, you gain less information. When you label the 1238th picture from the same cat you probably get almost no information at all. So, to optimize your time, you should probably label pictures from other individuals before you get to the 1238th picture.
How do you learn to do that in a principled way?
Active Learning is a task where instead of first labeling the data and then learning a model, you do both simultaneously, and at each step you have a way to ask the model which next example should you manually classify for it to learn the most. You can than stop when you're already satisfied with the results.
You could think of it as a reinforcement learning task where the reward is how much you'll learn for each label you acquire.
The reason why, as a Bayesian, I like active learning, is the fact that there's a very old literature in Bayesian inference about what they call Experiment Design.
Experiment Design is the following problem: suppose I have a physical model about some physical system, and I want to do some measurements to obtain information about the models parameters. Those measurements typically have control variables that I must set, right? What are the settings for those controls that, if I take measurements on that settings, will give the most information about the parameters?
As an example: suppose I have an electric motor, and I know that its angular speed depends only on the electric tension applied on the terminals. And I happen to have a good model for it: it grows linearly up to a given value, and then it becomes constant. This model has two parameters: the slope of the linear growth and the point where it becomes constant. The first looks easy to determine, the second is a lot more difficult. I'm going to measure the angular speed at a bunch of different voltages to determine those two parameters. The set of voltages I'm going to measure at is my control variable. So, Experiment Design is a set of techniques to tell me what voltages I should measure at to learn the most about the value of the parameters.
I could do Bayesian Iterated Experiment Design. I have an initial prior distribution over the parameters, and use it to find the best voltage to measure at. I then use the measured angular velocity to update my distribution over the parameters, and use this new distribution to determine the next voltage to measure at, and so on.
How do I determine the next voltage to measure at? I have to have a loss function somehow. One possible loss function is the expected value of how much the accuracy of my physical model will increase if I measure the angular velocity at a voltage V, and use it as a new point to adjust the model. Another possible loss function is how much I expect the entropy of my distribution over parameters to decrease after measuring at V (the conditional mutual information between the parameters and the measurement at V).
Active Learning is just iterated experiment design for building datasets. The control variable is which example to label next and the loss function is the negative expected increase in the performance of the model.
So, now your procedure could be:
Or you could be a lot more clever than that and use proper reinforcement learning algorithms. Or you could be even more clever and use "model-independent" (not really...) rewards like the mutual information, so that you don't over-optimize the resulting data set for a single choice of model.
I bet you have a lot of concerns about how to do this properly, how to avoid overfitting, how to have a proper train-validation-holdout sets for cross validation, etc, etc, and those are all valid concerns for which there are answers. But this is the gist of the procedure.
You could do Active Learning and iterated experiment design without ever hearing about bayesian inference. It's just that those problems are natural to frame if you use bayesian inference and information theory.
About the jargon, there's no way to understand it without studying bayesian inference and machine learning in this bayesian perspective. I suggest a few books:
Is a pretty good introduction to Information Theory and bayesian inference, and how it relates to machine learning. The Machine Learning part might be too introductory if already know and use ML.
Some people don't like this book, and I can see why, but if you want to learn how bayesians think about ML, it is the most comprehensive book I think.
More of a philosophical book. This is a good book to understand what bayesians find so awesome about bayesian inference, and how they think about problems. It's not a book to take too seriously though. Jaynes was a very idiosyncratic thinker and the tone of some of the later chapters is very argumentative and defensive. Some would even say borderline crackpot. Read the chapter about plausible reasoning, and if that doesn't make you say "Oh, that's kind of interesting...", than nevermind. You'll never be convinced of this bayesian crap.
Grad school for machine learning is pretty vague, so here's some general resources I think would be good for an incoming CS grad student or undergraduate CS researcher with a focus on deep learning. In my opinion, the courses you mentioned you've done should be a sufficient foundation to dive into deep learning, but these resources cover some foundational stuff as well.
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.
Disclaimer before blowing your mind even further: Mentifex here wrote the following Strong AI e-books. Singularity in the "Psyborg" series is the inital exposition about Strong AI entities (designed by Mentifex) taking over humanoid robots in science museums and trying to recruit a human philosophy graduate to help the Strong AI Overmind do battle with the National Security Agency from the bowels of the Utah Data Center in Bluffdale, Utah.
InFerence is both a technical book about automated reasoning and also a philosophic treatment of Strong AI in brief chapters about the
Wotan Strong AI in German; the Душка AI in Russian; Strong AI Superintelligence; and the Technological Singularity. As part of a memetic campaign to bring about the Singularity with Strong AI, InFerence appears worldwide in Brazil,
Canada, France, Germany, India, Italy, Japan, Mexico, Spain, United Kindgom and the United States.
Hi!
I am also studying ML/AI ( who isn't these days).
I have already taken some math courses ( I am a math teacher) , but this was like 10 years ago.
If not I think I would have started with more math.
​
I have based my own curriculum around these three:
​
​
Courses/books that I have done or started:
​
Doing right now:
Continue with MITx's Statistics and Data Science MicroMasters. I don't think I will take the Data Analysis course in it though.
Im starting a deep learning course today. Probably Deep Learning A-Z™: Hands-On Artificial Neural Networks. I feel that I need to do at least 1 course in deep learning and Reinforcement learning before finishing the Microsoft AI program.
​
Can recommend all of the courses/books above. They complement each other quite well. I have also done the Getting started on kaggle.com, which was fun.
​
If you have any suggestions for further reading/study please let me know!Besides following my on curriculum Im also studying a three year CS-program (starting my second year soon).
Hierarchical hidden markov models can achieve what the human brain does, and may in fact be the underlying abstract processes of the brain. Both things seem plausible to me.
Creating something that "thinks" well with HHMM is certainly possible. And I think if consciousness is really (as some propose) an innate property of all complex systems which reflect back on themselves, creating a mind which is conscious may take no special extra effort. Cristoph Koch has some interesting observations here.
Personally I think the primary challenge with HHMM for creating something like the human mind is not the difficulty of creating a tall and wide enough hierarchy, it's finding an appropriate sensory apparatus for an AI, so it might resemble something we would recognize. I think the human body plays a very large role in the human mind -- Perhaps even that the human mind physiologically extends farther into the nervous system and body than we usually give it credit. And our minds are tailored to exist in these bodies and to be very concerned with food and sex and physical power. It precedes and permeates our bodies and minds. An AI evolved without the symbiotic apparatus of a body like ours, which is the product of billions of years of trial in the physical and reproductive world, may be very alien to us in ways we do not expect.
Off-course R is used for machine learning. It's probably the most popular language for interactive exploratory and predictive analytics right. For instance most winners of kaggle.com machine learning competitions use R at one point or another (e.g. packages such as randomForest, gbm, glmnet and off-course ggplot2). There is also a recent book specifically teaching how to use R for Machine Learning: Machine Learning for Hackers.
My-self I am more a Python fan so I would recommend python + numpy + scipy + scikit-learn + pandas (for data massaging and plotting).
Java is not bad either (e.g. using mahout or weka or more specialized libraries like libsvm / liblinear for SVMs and OpenNLP / Standford NLP for NLP).
I find working in C directly a bit tedious (esp. for data preparation and interactive analysis) hence better use it in combination with a scripting language that has good support for writing C bindings.
+1 for top-down learning approaches. There's so much work going on to democratize use of ML techniques in general software development, that, depending on where you want to go, there's little need to start with the classic theory.
IMHO, the classic ML literature suffers a bit from decades of theorists who never had the computing resources (or the data) to make big practical advances, and it tends to be overly dense and mathematical because that's what they spent their time on.
But really it depends on your goals. Which category do you fall into?
For #1 the Super Harsh Guide is, well, super harsh, but has good links to the bottom up mathematical approach to the whole thing.
For #2 you should probably start looking at the classic ML techniques as well as the trendy Deep Learning stuff. You might enjoy:
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
as a place to start and immediately start playing around with stuff.
For #3 any of the TensorFlow getting started tutorials are good, along with all of Martin Görner's machine learning/deep learning/TensorFlow "without a PhD" videos on YouTube. Here's one of the more recent ones:
https://www.youtube.com/watch?v=vaL1I2BD_xY
It really depends on your comfort and familiarity with the topics. If you've seen analysis before you can probably skip Rudin. If you've seen some functional analysis, you can skip the functional analysis book. Convex Optimization can be read in tandem with ESL, and is probably the most important of the three.
Per my other comment, if your goal is to really understand the material, it's important you understand all the math, at least in terms of reading. Unless you want to do research, you don't need to be able to reproduce all the proofs (to help you gauge your depth of understanding). In terms of bang for your buck, ESL and Convex Optimization are probably the two I'd focus on. Another great book Deep Learning this book is extremely approachable with a modest math background, IMO.
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!
There have already been a few books listed focusing on theory, so I'll add Machine Learning for Hackers to the list.
It doesn't cover much of the theory, but it's a nice start to getting the programming skills you need for machine learning. When you start using these techniques on real data, you'll quickly see that it's almost never a simple task to go from messy data to results. You need to learn how to program to clean your data and get it into a usable form to do machine learning. A lot of people use Matlab, but since they're free I do all of my programming in R and Python. There are a lot of good libraries/packages for these languages that will enable you to do a lot of cool stuff.
That was a phenomenal article. Extremely long (just like every piece of writing associated with Hofstadter), but excellent nonetheless. I'm admittedly sympathetic to Hofstadter's ideas, not the least of which because of my combined math/cognitive science background.
There was a quote by Stuart Russell, who helped write the book on modern AI, that really stood out to me, and I think expresses a lot of my own issue with the current state of AI:
“A lot of the stuff going on is not very ambitious... In machine learning, one of the big steps that happened in the mid-’80s was to say, ‘Look, here’s some real data—can I get my program to predict accurately on parts of the data that I haven’t yet provided to it?’ What you see now in machine learning is that people see that as the only task.”
This is one of the reasons I've started becoming very interested in ontology engineering. The hyperspecialization of today's AI algorithms is what makes them so powerful, but it's also the biggest hindrance to making larger, more generalizable AI systems. What the field is going to need to get past its current "expert systems" phase is a more robust language through which to represent and share the information encoded in our countless disparate AI systems. \end rant
I think it is completely possible. I'm ML engineer with M.Sc. in Computer Science. Presently, there are so many avenues (MOOCs, Kaggle, and books) to learn ML. But I believe the best approach would be:
I mean, only if you think we're tens of thousands of years away from powerful, general-purpose software agents. If you survey actual experts, they're pretty uncertain (and vulnerable to framing effects) but in general they think less than a century is pretty plausible.
So it's closer to somebody looking at the foundational research in nuclear physics and going "hey guys, this is going to be a real fucking problem at some point."
Which is pretty much what Einstein did (and started the Manhattan project and a pretty significant intelligence operation against the development of a German nuclear weapon).
EDIT: Also, if anyone's interested, the same blogger made a rundown of the opinions of lumaries in the field on AI risk in general. Opinions seem to be split, but there are plenty of bright people who know their shit who take the topic seriously. For those who aren't familiar with the topic and think everyone's just watched too much bad sci-fi, I recommend Bostrom.
I would make a distinction to what are "complex" algorithms/methods towards simple/basic methods. You seem to be at a stage in which it's better for you to discard all the complex methods, and maybe just focus on the simple and basic methods. Simple because they do not require a lot of mathematical knowledge and basic because further theory is built upon them. This would exclude, for now, all the recent published literature.
I would suggest you to get one book that will ease this process, such as Bishop's. Just start with the basics of maximum likelihood and posterior inference estimation with simple Gaussians. I assure you that this is basic, in the sense that you will recognize and use this piece of knowledge in most advanced papers. Mixture of Gaussians and the EM algorithm are also a basic topic, as well as Neural Networks (the simple sigmoid fully connected).
Just make sure that you know these three topics extremely well and learning the rest will be slightly easier.
BTW, this is a post for /r/MLQuestions or /r/learnmachinelearning
Consciousness by Christof Koch is an excellent short read. The author is the chief science officer of the Allen Institute for Brain Research, which makes it feel a little more credible than other pop-neuroscience books.
I am a Strange Loop by Douglas Hofstadter Covers a lot of the same ideas as GEB, but more succinctly.
+1 for Vision by David Marr and The Computer and the Brain by von Neumann mentioned by others.
Also it's definitely a more technical book, but I've really enjoyed reading Information Theory, Inference, and Learning Algorithms by David MacKay. It explains many of the relationships between
info theory, Bayesian statistics, machine learning, and computational neuroscience.
Well I do research in pattern recognition and computer vision so I'll try to answer this. An image is a grid of sensor readings. Each reading from a sensor is called a pixel which is the feature vector for that location in the image plane. Features based on spectral characteristics, spatial characteristics, and even motion characteristics (in video) may be derived from the original input (the reading from the sensor). Transformations are applied to the input which consider different aspects of the pixel's spectral components ( [R,G,B] - tristimulus ). A number of different methods exploit spatial correlation too. These features are then used in ML systems as part of the feature vector ( [T1,T2,T3,F1,F2,F3,F4,...] ). As far as books, I learned filtering methods using
"Two-Dimensional Signal and Image Processing" -Lim
I learned pattern recognition using
"Pattern Recognition" -Theodoridis and Koutroumbas
and
"Pattern Recognition and Machine Learning" -Bishop
The last one approaches from more of a CS side but doesn't go as in-depth. The field of CV/PR is pretty large and includes a lot of methods that aren't covered in these books. I would recommend using OpenCV or Matlab to handle images. My personal preference is Python but C++ and Matlab is are both close seconds.
If you want super beginner, Data Smart by John Foreman is probably the best. It isn't free and it is very basic.
http://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X
Creating links from neuroscience to ANN learning is actually a hot research topic at the moment so you re in luck. A popular theme is coming up with ways that backpropagation can be implemented by biological neurons e.g. 1 2 3
However don't expect to find a lot of theory about how brains learn. While comp. neuro has a good model for the voltage response and propagation in neurons 4, the knowledge about the neurobiology of learning is very sketchy so you are going to have to become familiar with the learning and memory experimental literature and probably develop your own models.
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
I kind of see your point, but I don't completely agree. As I said already, I know something about active research in this field: enough, as a matter of fact, to be able to read these books
https://www.amazon.com/Understanding-Machine-Learning-Theory-Algorithms/dp/1107057132
https://www.amazon.com/Foundations-Machine-Learning-Adaptive-Computation/dp/0262039400/
https://www.amazon.com/High-Dimensional-Probability-Introduction-Applications-Probabilistic/dp/1108415199/
However, as most researchers, I mostly focus on my specific subfield of Machine Learning. Also, every now and then, I'd like to read something about my job, which however doesn't feel like work (even a professional football player may want to kick a ball for fun every now and then 😉). Thus, I was looking for some general overview of Machine Learning, which wouldn't be too dumbed down, according to experts (otherwise I wouldn't have fun reading it), but which at the same time wasn't a huge reference textbook. After all, this would be just a leisure read, it shouldn't become work after work.
That's why I asked here, rather than on r/LearnMachineLearning. However, if other users also feel I should ask there, I will reconsider.
The Udacity machine learning track that you've probably seen is actually wonderful. It does a good job of scaling from entry level (even going down to basic data analysis) up to DNN. They charge for the nano-degree, but you can access all of the lectures without that.
As far as reading papers, I would actually recommend against it at this point. They're highly minute unless you're actually doing research into new techniques. If you're mostly looking to build a portfolio for employers, not a good place. If you're looking for a reading source Bishop's Machine Learning and Pattern Recognition is one of my favorites.
O'Reilly has published a number of practical machine learning books such as Programming Collective Intelligence: Building Smart Web 2.0 Applications and Natural Language Processing with Python that you might find good starting points.
You need a developer account to scrape data through their API otherwise you'll hit the limit pretty soon. This book has a case study on what you're looking for:
https://www.amazon.com/Mastering-Social-Media-Mining-Python/dp/1783552018/ref=sr_1_1_sspa?keywords=mastering+social+media+python&qid=1568867389&s=gateway&sr=8-1-spons&psc=1&spLa=ZW5jcnlwdGVkUXVhbGlmaWVyPUEzMUkzUEFPQkYwS1k3JmVuY3J5cHRlZElkPUEwOTkwMDg5V1EzQ1Q3REkwNEFZJmVuY3J5cHRlZEFkSWQ9QTA3MzYxMzgzN0NDTDE1QUFYNUZKJndpZGdldE5hbWU9c3BfYXRmJmFjdGlvbj1jbGlja1JlZGlyZWN0JmRvTm90TG9nQ2xpY2s9dHJ1ZQ==
​
Plus there are some really cool python libraries like tweepy. Check them out.
Data Smart
Whole book uses excel; introduces R near the end; very little math.
But learn the theory (I like ISLR), you'll be better for it and will screw up much less.
Personally, I like "Introduction to Machine Learning" by Alpaydin.
I also strongly recommend reading "The Computational Complexity of Machine Learning" by Michael Kerns.
I agree with @machinedunlearned on the point that ML is a multidisciplinary field. I've been doing work in this field for several years, and I don't consider myself a subject matter expert on much outside of what I call Intelligent Systems. As such, I tend to get "tied at the hip" to a field expert when I'm applying ML to various problems. That said, note that ML has a wide range of techniques that are ever expanding since it's a hot area of research. First gain a broad understanding of what constitutes supervised, unsupervised, and reinforcement learning, then lean when each type of learning is best applied to various problems. That skill will prove invaluable. The references will touch on this some, but don't be afraid to try something different to learn something new!
It's only $72 on Amazon. It's mathematical, but without following the Theorem -> Proof style of math writing.
The first 1/3 of the book is a review of Linear Algebra, Probability, Numerical Computing, and Machine Learning.
The middle 1/3 of the book is tried-and-true neural nets (feedforward, convolutional, and recurrent). It also covers optimization and regularization.
The final 1/3 of the book is bleeding edge research (autoencoders, adversarial nets, Boltzmann machines, etc.).
The book does a great job of foreshadowing. In chapters 4-5 it frames problems with the algorithms being covered, and mentions how methods from the final 1/3 of the book are solving them.
https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/
Anyway, a great book often recommended is All of statistics. You can't go wrong with that book.
I would very intensely recommend the SSD ! Once you've tried it, you can't go back ;)
And it's not that expensive for a 1080Ti owner :)
https://www.amazon.com/SanDisk-240GB-Solid-State-SDSSDA-240G-G26/dp/B01F9G43WU/ref=sr_1_3?s=pc&ie=UTF8&qid=1539724004&sr=1-3&keywords=ssd+240gb
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.
You can read through a machine learning textbook (Alpaydin's and Bishop's books are solid), and make sure you can follow the derivations. Key concepts in linear algebra and statistics are usually in the appendices, and Wikipedia is pretty good for more basic stuff you might be missing.
I would start with reading.
For Neural Networks, I'd do:
For overview with NN, Fuzzy Logic Systems, and Evolutionary Algorithms, I recommend:
Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation (IEEE Press Series on Computational Intelligence) https://www.amazon.com/dp/1119214343/ref=cm_sw_r_cp_apa_i_zD11CbWRS95XY
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)
--------
http://people.seas.harvard.edu/~mgelbart/glossary.html
http://www.quora.com/Machine-Learning
http://www.quora.com/Machine-Learning-Applications
What worries me is that this advance happened 10 years earlier than it was supposed to. And the DeepMind guys think they could have human-level AI within a few decades.
In other words, it looks like human-level AIs may be something we encounter significantly sooner than we do "overpopulation on Mars", to quote Andrew Ng. I hope Ng is at least considering reading Superintelligence or signing the FLI AI Safety research letter.
Depends what your goal is. As you have a good background, I would not suggest any stats book or deep learning. First, read trough Probability theory - The logic of science and the go for Bishop's Pattern Recognition or Barbers's Bayesian Reasoning and ML. If you understand the first and one of the second books, I think you are ready for anything.
Read Hands on Machine Learning with Scikit-learn and Tensorflow. This book is awesome.
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
If you want to learn the algorithms by programming them you have Programming Collective Intelligence that is really good. It really helped me to see the algorithms in work in order to deeply understand them.
Ok, it's kinda expensive (~$60), but it's on amazon:
http://www.amazon.com/Probability-Theory-Logic-Science-Vol/dp/0521592712/ref=sr_1_1?ie=UTF8&s=books&qid=1252600006&sr=8-1
*edit for link pwnage.
You might find Wasserman's All of Statistics useful:
http://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/0387402721/
It's a very concise, yet broad introductory statistics text with a slant towards data mining/machine learning.
May I suggest doing a search in r/statistics and r/machinelearning for learning-foundation books for ML? I think that'll turn up quite enough hits to get you pointed in the right direction.
I always talk up the one I used, which I liked:
http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
Thats a $30 book
Cool!
I'd grab beautifulsoup + scikit-learn + pandas from continum.io (they're part of the standard anaconda download), launch Spyder and follow through this:
http://sebastianraschka.com/Articles/2014_naive_bayes_1.html
You can get a RAKE impl here too : https://github.com/aneesha/RAKE
Doing recommendations on the web like that is covered in an accessible way in "Programming Collective Intelligence"
Recsys is a broad subject, on the top of my mind:
Have a look at Xavier Amatriain MLSS lecture in 2014:
This book does a broad -> https://www.amazon.fr/Recommender-Systems-Textbook-Charu-Aggarwal/dp/3319296574
Look at models implemented in spotlight: https://github.com/maciejkula/spotlight
Look at recent Recsys papers.
This should get you started.
​
The first one isn't too off: Amazon link to a book
Granted, it's not distributed, but I read that book given its high rating and the author really jumps through hoops trying to figure out how you'd do k-means in a spreadsheet without macros or anything.
Is this the Bishop book you guys are talking about?
This:
http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
For a maths heavy book, I'd go with Bishop's Pattern recognition and Machine Learning.
Check out the reviews here: http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
Try going through the Kevin Murphy book : https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020
Real answer: https://www.amazon.co.uk/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020
Murphy
BRML
ESL
Are you referring to Machine Learning: A Probabilistic Perspective? (link to Amazon)
> I can't help but cringe every time he assumes that self-improvement is so easy for machines so that once it becomes possible at all, AI skyrockets into superintelligence in a matter of weeks.
He doesn't assume it, he concludes it after discussing the topic in depth.
Pages 75-94 of his book. Preview available via Amazon.
The Bostrom book is the go-to reference for the sort of ai risk arguments that Musk and others endorse. Elon has previously linked to this WaitBuyWhy post summarizing the argument from the book, so I would read that if you're curious.
(Not that I agree with any of it, but linking since you asked)