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I am wondering whether neural networks could help in monitoring the user request for micro services and also for monolithic service which will improve the performance of the productivity. I need a detailed advice about my query.
I have got this to know when reading this article. I am also interested in any other ideas that ML could help micro services or in monitoring server.
It depends ... on what you want to achieve. ML/"AI" is typically used to predict a specific outcome based on existing data. So, if there is historical data which indicate that the state if the system is {relaxed|critical}, you might get an idea when to act, before "critical" is reached. But then again, it appears to be be an overkill of you can simply just monitor your resources and define a threshold, when more resources need to be applied (cloud service provider scale on demand).
If you are thinking about anomaly detection, here is where ML/"AI" might help. But: you need to have relevant data to actually train a useful net.
My tip: check for service providers like datadog and check what they have in store for you. Training, evaluating and putting a neural net is not a trivial task.
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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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This might not be the right place for this to ask, but I am interested in artificial neural networks and want to learn more.
How do you design a network and train it on source code so it can come up with programs for, for example, easy number theory problems?
What's the general name of this research field?
This is a hugely interesting, and very hard, problem area. It will probably take you months to read enough to even understand how to attack the problem. Here's a few things that might help you get started, and they are more to show the problems you will face than to provide solutions:
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
Then read this, and related papers:
https://arxiv.org/pdf/1410.5401v2.pdf
Next, you probably want to read the classic papers in program synthesis and generation at the parse tree/AST level (mostly out of MIT, I think, in the early 90s.)
Best of luck. This is not trivial.
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(homework problem)
Which of the following problems are best suited for the learning approach?
Classifying numbers into primes and non-primes.
Detecting potential fraud in credit card charges.
Determining the time it would take a falling object to hit the ground.
Determining the optimal cycle for trafic lights in a busy intersection
I'm trying to answer your question without doing your homework.
Basically you can think of machine learning as a way to extract patterns from data where all other approaches fail.
So first clue here: If there is an analytic way to solve the problem then don't use machine learning! The analytic algorithm will likely be faster, more efficient, and 100% correct.
Second clue is: There has to be a pattern in the data. If you as a human see a pattern, machine learning can find it too. If lots of smart humans who are experts of the respective domain don't see a pattern then machine learning will most likely fail. Chaos can not be learned, i.e. classified/predicted.
That should answer your question. Make sure to also read the summary on wikipedia to get an idea whether a problem can be solved using supervised, unsupervised, or reinforcement learning.
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I need to write the backend for an event ticket selling system. Some companies would connect to the service in order to check for ticket availability in certain venues, book tickets and so on.
Reading about Erlang I though it could be a good choice since the system will have to support high concurrency, high availability but I don't know If it's a good choice for this problem domain.
Any help would be really appreciated. Thanks!
Erlang could be a good choice, yes, it sounds like something it would do a very good job.
But it's going to be hard for anyone here to be of much value for your decision, as you should also consider the knowledge level of the team, time & budget constraints, etc.
Ultimately, the best people to help you make this decision are the people in your team.
I suggest you take a look at OTP's finite state machines as I think it suits perfectly a ticket booking system.
I believe you can find a lot of examples either in the doc or on the web.
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Using data mining, we are able to find useful patterns in a large set of data using techniques like correlation etc etc and there must exist some open source tools for this (what are some examples?).
Is this pull-based or push-based? I mean, do we provide data set as well as specific queries as input to the data mining engine and it provides us answers (as in SQL) or we only supply large data set as input to the engine and it on its own find patterns (which we never knew existed and/or we couldn't formulate queries for this) and thus we don't really pull any specific queries from it, it pushes the patterns to us.
Some quick reading of Wikipedia article doesn't clarify my doubts in clear way.
As open source have a look at Weka.
In regards to the push-pull thing, well, it's a bit of both. But it's not quite that simple. You must be looking for something. E.g. if you are looking for clusters, there are unsupervised algorithms which will give you an answer with minimal guidance.
In practice things are more meaningful if you know about the data you analyse and you are looking at regularities and patterns that make sense.
Playing with Weka will give you a better idea of the range of possibilities.
Python and R are other great open source tools that have great popularity in the data mining area.
A great tool that i used recently is scikit-learn