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What should I use for stock market prediction and why? comparison if you can please.
Udpated: I wanted to use it for stock market movement (up,down) for 1 day.Also,Thank you for your answer it halped
It's not easy to say you which ML algo will give you best perfomance. Especially if not to see which market you want to predict. I recommend you to implement different algorithms and try to train them, because in my practice changing of layers gave different results. SVM sometime was also flexible enough. Also try to implement and check how your training will work on trained and untrained data in order to have really good results. Also analyze how machine learning will work on more predictable sequences ( aka sin, cos, polinomials, randow walks)
Additional field of investigation can be some technical analisis additions: moving averages, stochastics, candle chart patterns, Fibonacci levels.
And finally in order to get money don't rely only on neural network or SVM but use them in conjunction with some trading strategy. For example you can use some trading strategy which has perfomance 30 % and use ML in order to rise perfomance to 60 %
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I have a lot of constraints(in millions) and want to solve a mixed integer problem. Tried using PuLP and OR-Tools(with SCIP solver), but they failed to solve the problem. I tried using DuaLip for parallel/distributed computing using spark, but it doesn't seem to have good documentation for usage. How should I proceed further?
AFAIK there do not exist continuous LP solvers that do distributed computing. That would require some decomposition scheme. As LPs solve very fast we don't use those techniques anymore. An LP with a few million constraints is not very large these days. Quite often interior-point algorithms do quite well on these large problems (relative to Simplex algorithms). We solve LPs of this size on a completely routine basis on fairly standard hardware.
Your PuLP problem is likely more related to PuLP having problems generating the problem than the solver solving it. (Note that PuLP is not a solver). Python based modeling tools may be slower in generating easy LPs than the solver needs to solve it.
Note: the situation for MIP solvers is very different.
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I've tried matlab, but unfortunately it is not threaded. I've also tried eigen and although it is threaded and scales quite well, the single thread performance is a little worse than Matlab.
How can I multiply a general large sparse * dense matrix in the fastest way possible on the CPU (not GPU).
Use both. For a single threaded environment, run matlab routines, for multi-threaded, go with eigen. And keep tabs on new developments because for highly competitive fields like these, any advice you get here will be out of date in a month.
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neural networks are used to generalize and classify...
I have a little experience with classify digits...
Using neural nets to recognize handwritten digits
i want to use a network to check plastic parts.
I have a videostream of production from these plastic parts.
should i train the network with many videos of correct plastic parts to get positive output and random videos to get negative output?
If you have any books or links i would be happy to see them.
EDIT
It looks like i asked a bit stupid...
During production, wrong plastic parts can be created and these should be recognized by network. There are a lot of mistakes can happen during production, so i think
it only makes sense to train the network with correct plastic parts.
A convolution neural network would be my recommendation.
You should show individual parts with similar background and lighting.
The training has to be done on both good and bad parts - a sufficient random sampling of both. You should also set aside a test set once your CNN is trained so you can evaluate it.
You'll want to generate a confusion matrix from the test data so you'll know the rate of false positives, false negatives, correct, and incorrect classifications.
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I have seen straight through estimator (STE) in many Neural Network related papers e.g. this and this. But I cannot understand the concept. I wonder if anyone could explain STE or refer me to a simple resource?
A straight through estimator is a way of estimating gradients for a threshold operation in a neural network. The threshold could be as simple as the following function,
As we can see, the derivative of this threshold function will 0 and during back-propagation, the network will not learn anything since it gets 0 gradients and the weights won't get updated.
The concept of a straight through estimator is that you set the incoming gradients to a threshold function equal to it's outgoing gradients, disregarding the derivative of the threshold function itself. This has been shown to perform well in the results (Figure 2) in this paper you have referenced.
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I am going through Modelica libraries. I was wondering, that there are (or i can't found them) ODE's.
For example in the dynamic pipe model of the standard library.
Generally, the Modelica Standard Library contains many dynamic models - i.e. models that can be described with DAEs or ODEs (look for models using the der() operator).
The reason why you don't see any der() operators in DynamicPipe is that it inherits much of its functionality from several base classes - including Modelica.Fluid.Interfaces.PartialDistributedVolume where you will find the differential equation for mass and energy balances.
Modelica.Blocks.Continuous.FirstOrder is an example of a very simple ODE - a first-order low-pass filter.
You might want to consult the free online Modelica book "Modelica by Example". It shows many examples involving ordinary differential equations.