Under my question, it is clearly stated
The question you're asking appears subjective and is likely to be closed
My apologies if my question is subjective to the point where it is closed. I didn't not mean to violate or break any rules.
I have a bunch of E-3 machines that can be utilized to mine but they do not have GPU's attached to them. They are 1U racked server machines.
Can someone be a sport and point me ion the right direction?
You can start with CPUMiner
AFAIK, currently Monero mining with Cryptonight algorithm is most profitable.
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I was tasked with creating a machine learning algorithm that receives vibration signals and uses them to determine if a machine is going to fail. With that said, I have a small problem.
I am not sure which machine learning algorithm I need to generate this.
Furthermore, any resources that would help me implement said algorithm would be greatly appreciated. I don't have a dataset to work with for the time being so I am currently just drafting ideas.
In the abstract, this is not a terribly complicated problem. First, you'll have to decide/understand what length of time before failure is useful for you circumstances. Do you need to detect failure a week in advance? Or a day in advance? Or an hour in advance? (Of course, you may not be able to predict effectively at any of those time lengths.) Use the past to predict the future; use what you'll have available to predict what you don't know.
This may be a simple problem, may not really require any machine learning at all. BUT, your big problem now isn't methodology. It's lack of data.
You'll need to start recording data and exploring it before you can move forward.
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I have the skills with C++, ruby,rails, and some other scripting languages. I want to start experimenting and learn concepts in Data mining and machine learning.
I want to be well equipped with the programming knowledge required for those concepts.
Which language has best support for DM and ML? puthon, Java, C++? Is there anything coming up in Javascript?
Thanks
Use whatever you are most comfortable with.
At least if it has the basics around. JavaScript and PHP are not very good in this domain. They just don't scale well for numerical computations. Python, R, Java and Scala are most popular. Theres Matlab, but there is not much happening there anymore. There is Julia which has a similar syntax but which is much more alive and shows some promise, if it weren't for colum-major,1-indexed arrays and the matlab-like syntax. Some use Lua, others Mathematica...
There are many many factors that play a role.
For example, scripting languages like Python and R are really slow - but these two also interface very well to C libraries (and Fortran!) so if you nostly use them as a "driver" and the libraries do all the work then they can be very usable. Just make sure to not assume every module is fast...
I think that perhaps your question is a bit off target. The languages themselves don't generally have the support: it's packages that interface with those languages, such as Apache Spark (interface to their ML package), Intel's MKL (vector and matrix operations optimized for Xeon Phi), SciKit (Python interface), etc.
That said, I see the most active support for languages that drive at distributed processing. In my ambit, Java/Spark is currently the front-runner, but one or two major releases can change the market considerably -- see the buzz on Tensor, for instance, or the staying power of BeautifulSoup.
For experimentation, start with your comfort zone. There are plenty of good tools that interface well with Ruby and C++, as well. As long as you're using this to learn the underlying concepts, I believe that you'll do best with a language you already know: that gives you one less area of frustration in your learning curve.
Anony-Mousse and Patricio have given you very good points with which I totally concur. I'm working in Python and Scala, with Java and Spark just underneath.
Python has a very strong support from the Data Science Community, you have very good packages like Pandas and Python has a very good integration with Spark
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If so, what is the storage and memory footprint?
EDIT
I had done some research about this, but failed to find useful information. The site http://www.erlang-embedded.com/ doesn't help at all. The blog article http://www.1011ltd.com/web/blog/post/embedded_erlang was a little helpful, but It would be nice to hear answers from people with more experience.
EDIT 2
The hardware that I intend to use for Erlang has 32Mb of FLASH storage for the system and 512Mb of RAM. It is dual core with 400Mhz per core. It runs Linux version 2.6.18.
EDIT 3
The motivation behind my interest in Erlang would be to solve gracefully concurrency problems. On the project that I work we have some complex middleware software that is not robust, it's hard to understand and hard to extend. Of course, you can write great concurrent software in C, but Erlang just seems like a better tool for this problem domain.
What is embedded for you?
In my world it's a system with less than 1MB Flash and typically ~64kB Ram.
In my world exists C and sometimes also C++ compilers.
But nobody heard ever for an erlang compiler for such a system (and nobody missed them).
But if embedded is for you WindowsCE or a linux running on a non PC basis hardware with > 64MB Ram and 1GB Flash,
then there should be no problem with erlang.
I would echo the sentiment that the question is vague. But, ...
Not trying to troll, but I think the answer is either "Yes!!" or "No!!" depending on your assumptions regarding hardware and what problems your are trying to solve that aren't easily solved by something more standard like C (i.e., why aren't you using something like C, there must be a reason... reducing code-size, need hot-upgrade, {erlang_value_prop, n}, etc.).
Under a certain set of criteria, the answer seems to be "yes". Evidence includes:
EMBEDDED ERLANG? ABSOLUTELY (http://www.1011ltd.com/web/blog/post/embedded_erlang)
Its embedded use in ATM switches and other telecom equipment
There is (or was) an embedded-Erlang group on Google
I think Ulf Wiger has an Embedded Erlang slide-deck as part of his work with Erlang Solutions
etc
No,
Many embedded systems don't have Erlang compilers, while all have C compilers and most have C++.
Erlang lacks the low level access required by an embedded system.
Its certainly possible to get Erlang on a cluster of Raspberry Pis, but this isn't an embedded device.
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I do not mean English. Just communication. I have this problem in my team that we are often discussing complicated topics, be it face to face, in emails, during meetings or in our issue tracker, and people often find it difficult to stay focused and understand each other.
What are the best resources (books, presentations) on that topic? Is there any way one can learn this quickly?
For your personal growth:
-take the intiative to be in situations that require this. Join the army! :D But on a more practical note: join a theatre group, start writing/casting a blog, TA some students (officially or not). Simply practicing this often (and getting the immediate feedback through the reaction of other people), you'll start noticing what is more effective and get in the habit of doing it.
-I recommend the book "On writing well.", William Zinnser. Well written and concise, and short enough that you have the time for it, and most concepts can be applied to communication in general, not just writing.
Note that even though it is quite easy to understand the concepts, this is very much a matter of charachter, so it'll take a while for your effort to become habit. Worth it though.
Are you also looking for ideas for your current situation, or just resources?
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I have a project to recognize the footprint of animals. It is similar to facial recognition.
There is a need to store footprint images in a database and compare them with images captured by camera.
What is the appropriate programming language to do this?
Any language can be used for image processing, pattern recognition and object detection, which is what you're trying to do here. But you're better off finding a library or even an application instead, and then picking the language based on that choice.
Matlab is fine if you're familiar with it, unless you plan on delivering a working system that will be used by others to add or annotate data. In that case, you'll need something easier to deploy beyond your own workstation.
OpenCV might be a good place to start, and there's an OpenCV tutorial here.
Since it's a similar problem, you may want to check out the Face Recognition Homepage for more detailed information.
I think the question is rather how you represent the data and determine likeness/sameness/distance measuring rather than an implementation language.
Lisp is a strong candidate, as is C/C++ - but really you are probably better off with whatever language you/your team knows best.
Again, figure out the data representation first.
Also - find another imaging/matching solution out there. There are already ones for license plates, fingerprint, etc - and maybe just use that source. The roblem is mostly solved...
If you need to get something working quickly, I would suggest Matlab or some similar math package. There are a lot of built-in algorithms that you can use for image processing and rapid prototyping.
Your biggest problem here is developing the algorithm, not choosing the language. My advice would be to prototype your project in Matlab, if you have access to it. What you are trying to do is an active area of research, and many researchers prefer Matlab and publish their Matlab code. This means that you may be able to find Matlab code on the web that may do at least some of what you need, such as image segmentation.
I would advise against using C++, unless you actually get your algorithm to work, and speed becomes important. Matlab would allow you to quickly try out ideas, and avoid spending most of your time on implementation details. Once you develop your algorithm to the point when you are happy with the results, then you can think about implementing it as a usable system in a "real" programming language.