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I'm new to machine learning and trying to figure out where to start and how to apply it to my app.
My app is pulling a bunch of health metrics and based on all of them is suggesting a dose of medication (some abstract medication, doesn't matter) to take. Taking a medication is affecting health metrics and I can see if my suggestion was right of if it needs adjustments to be more precise the next time. Medications are being taken constantly so I have a lot of results and data to work with.
Does that seem like a good case for machine learning and using some of neural networks to train and make better predictions? If so - could you recommend an example for Tensorflow or Keras?
So far I only found image recognition examples and not sure how to apply similar algorithms to my problem.
I'm also a beginner into machine learning, but based on my knowledge, one way would be to use supervised learning with Keras, which uses Tensorflow as a backend. Keras is a lot easier to program than Tensorflow, but eventually Tensorflow might as well do the trick (depending on your familiarity with machine learning libraries).
You mentioned that your algorithm suggests medication based on data (from the patient).
One way to predict medication is to store all your preexisting data in a CSV file, and use the CSV module to read it. This tutorial covers the basics of reading CSV files (https://pythonprogramming.net/reading-csv-files-python-3/).
Next, you can store the data in a multi-dimensional array, and run a neural network through it. Just make sure that you have sufficiently enough data (the more the better) in comparison with the size of your neural network.
Another way, as you mentioned, would be using Convolutional Neural Networks, which theoretically could and should work, but I have very little experience programming them, so I'm afraid I can't give you any advice for that (you can program CNNs in both Keras and Tensorflow).
I do wish you good luck in your project!
I'm doing a project to detect (classify) human activities using a ARM cortex-m0 microcontroller (Freedom - KL25Z) with an accelerometer. I intend to predict the activity of the user using machine learning.
The problem is, the cortex-m0 is not capable of processing training or predicting algorithms, so I would probably have to collect the data, train it in my computer and then embed it somehow, which I don't really know how to do it.
I saw some post in the internet saying that you can generate a matrix of weights and embed it in a microcontroller, so it would be a straightforward function to predict something ,based on the data you providing for this function. Would it be the right way of doing ?
Anyway my question is, how could I embedded a classification algorithm in a microcontroller?
I hope you guys can help me and give some guidance, I'm kind of lost here.
Thank you in advance.
I've been thinking about doing this myself to solve a problem that I've had a hard time developing a heuristic for by hand.
You're going to have to write your own machine-learning methods, because there aren't any machine learning libraries out there suitable for low-end MCUs, as far as I know.
Depending on how hard the problem is, it may still be possible to develop and train a simple machine learning algorithm that performs well on a low-end MCU. After-all, some of the older/simpler machine learning methods were used with satisfactory results on hardware with similar constraints.
Very generally, this is how I'd go about doing this:
Get the (labelled) data to a PC (through UART, SD-card, or whatever means you have available).
Experiment with the data and a machine learning toolkit (scikit-learn, weka, vowpal wabbit, etc). Make sure an off-the-shelf method is able to produce satisfactory results before moving forward.
Experiment with feature engineering and selection. Try to get the smallest feature set possible to save resources.
Write your own machine learning method that will eventually be used on the embedded system. I would probably choose perceptrons or decision trees, because these don't necessarily need a lot of memory. Since you have no FPU, I'd only use integers and fixed-point arithmetic.
Do the normal training procedure. I.e. use cross-validation to find the best tuning parameters, integer bit-widths, radix positions, etc.
Run the final trained predictor on the held-out testing set.
If the performance of your trained predictor was satisfactory on the testing set, move your relevant code (the code that calculates the predictions) and the model you trained (e.g. weights) to the MCU. The model/weights will not change, so they can be stored in flash (e.g. as a const array).
I think you may be limited by your hardware. You may want to get something a little more powerful. For your project you've chosen the M-series processor from ARM. This is the simplest platform that they offer, the architecture doesn't lend itself to the kind of processing you're trying to do. ARM has three basic classifications as follows:
M - microcontroller
R - real-time
A - applications
You want to get something that has strong hardware support for these complex calculations. You're starting point should be an A-series for this. If you need to do floating point arithmetic, you'll definitely need to start with the A-series and probably get one with NEON-FPU.
TI's Discovery series is a nice place to start, or maybe just use the Raspberry Pi (at least for the development part)?
However, if you insist on using the M0 I think you might be able to pull it off using something lightweight like ROS-C. I know there are packages with ROS that can do it, even though its mainly for robotics you may be able to adapt it to what you're doing.
Dependency Free ROS
Neural Networks and Machine Learning with ROS
I'm trying to use machine learning algorithms for repetitive form filling.
Here is a picture to illustrate that a little bit.
If you enter values in field A and B i would like to have a suggestion for field C.
For this case i really would like to implement a Machine learning algorithm so that the system stays really flexible and only makes suggestions by the knowledge that was build.
I've already started reading programming collective intelligence and Artificial intelligence a modern approach. I also started to play around with Weka a little bit and found a pretty good microsoft research paper on my problem too. But my main problem is that I can't really identify what algorithm group I should use. I'm primarily looking at Descision trees like C 4.5 but I'm not sure if this is the right way. Could you please give me any suggestions on my problem?
It looks like you're starting out... good luck.
Go for a Huffman tree / Genetic algorithm randomizer... for a quick solution.
Go for implementing everything you can think of, then using an external efficacy classifier to figure out what to use for the next iteration, and randomize something along the way.... for the more complex solution.
Decision trees are incredibly inflexible when it comes to this type of stuff. Try fuzzy logic algorithms.
I asked myself the question whether most people normally code the machine learning algorithms themselves or whether they are likely to use existing solutions like Weka or R packages.
Of course it depends on the problem - but let's say that I want to use a common solution like a neural network. Is there still a reason to code it myself? To understand the mechanism better and adapt it? Or is the thought of standardized solutions more important?
This is not a good question for Stackoverflow. It's an opinion question, not a programming problem.
Nevertheless, here is my take:
It depends on what you want to do.
If you want to find which algorithm works best for your data problem at hand, try ELKI, Weka, R, Matlab, SciPy, whatever. Try out all the algorithms you can find, and spend even more time on preprocessing your data.
If you know which algorithm you need and need to get it into production, many of these tools will not perform good enough or be easy enough to integrate. Instead, check if you can find low level libraries such as libSVM that provide the functionality you need. If these don't exist, roll your own optimized code.
If you want to do research in this domain, you are best off with extending the existing tools. ELKI and Weka have APIs that you can plug into to provide extensions. R doesn't really have an API (CRAN it's a mess...) but people just dump their code somewhere and (hopefully) add a manual how to use it. Extending these frameworks can save you a lot of effort: you have comparison methods ready to use, and you can re-use a lot of their code. ELKI for example has a lot of index structures to accelerate algorithms. Most of the time, the index acceleration is much harder to write than the actual algorithm. So if you can reuse the existing indexes, this will make your algorithms much faster, too (and you will also benefit from future enhancements to these frameworks).
If you want to learn about existing algorithms you better implement them yourself. You'll be surprised how much more there is to optimizing some algorithms than what is taught in class. E.g. APRIORI. The basic idea is quite simple. But getting all the pruning details right, I say 1 out of 20 students gets these details. If you implement APRIORI, then benchmark it against a known good implementation and try to understand why yours is much slower, then you'll actually discover the subtle details to the algorithms. And don't be surprised to see a factor of 100 performance difference between ELKI, R, Weka etc. - it's can still be the same algorithm, just implemented more or less efficiently when it comes to actual data structures used, memory layout etc.
I am working on testing several Machine Learning algorithm implementations, checking whether they can work as efficient as described in the papers and making sure they could offer a great power to our statistic NLP (Natural Language Processing) platform.
Could u guys show me some methods for testing an algorithm implementation?
1)What aspects?
2)How?
3)Do I have to follow some basic steps?
4)Do I have to consider diversity specific situations when using different programming languages?
5)Do I have to understand the algorithm? I mean, does it offer any help if I really know what the algorithm is and how it works?
Basically, we r using C or C++ to implement the algorithm and our working env is Linux/Unix. Our testing methods only focus on black box testing and testing input/output of functions. I am eager to improve them but I dont have any better idea now...
Great Thx!! LOL
For many machine learning and statistical classification tasks, the standard metric for measuring quality is Precision and Recall. Most published algorithms will make some kind of claim about these metrics, or you could implement them and run these tests yourself. This should provide a good indicative measure of the quality you can expect.
When you talk about efficiency of an algorithm, this is usually some statement about the time or space performance of an algorithm in terms of the size or complexity of its input (often expressed in Big O notation). Most published algorithms will report an upper bound on the time and space characteristics of the algorithm. You can use that as a comparative indicator, although you need to know a little bit about computational complexity in order to make sure you're not fooling yourself. You could also possibly derive this information from manual inspection of program code, but it's probably not necessary, because this information is almost always published along with the algorithm.
Finally, understanding the algorithm is always a good idea. It makes it easier to know what you need to do as a user of that algorithm to ensure you're getting the best possible results (and indeed to know whether the results you are getting are sensible or not), and it will allow you to apply quality measures such as those I suggested in the first paragraph of this answer.