I'm looking for tutorial on creating bayesian network. I have theoretical information and background but I would like to see it in practise on some real-life example.
Could you recommend me some such tutorial? Thank you.
There is an online course called "Probabilistic Graphical Models" by Stanford professor, Daphne Koller:
https://www.coursera.org/course/pgm
Click on "Preview" to watch the past lectures.
The Stanford course page is here, with a link to the textbook:
http://pgm.stanford.edu
You can see a step by step example on how to create one # http://unbbayes.sourceforge.net/video_tutorial.html. This is a link to video tutorials that explain how to use UnBBayes, an open source tool for modeling Probabilistic Graphical Models, most related to Bayesian Networks.
One of the videos from the link above is the youtube video # https://www.youtube.com/watch?feature=player_embedded&v=ExlfjBQfvMk, which explains how to create a BN step by step using UnBBayes.
Cheers,
Rommel
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I am trying to build an outdoor smoke detection from the neighbor chimneys.
I live in a neighborhood where a couple of houses are still using wood-burning fireplaces and cause lots of smoke and they do during the day time. when it is smoky outside, the kid's room sometime has windows open and smoke get in and very hard to get smoke out. The worst part is it is not illegal (yet) so I found little help apart from talking to them and react to it quickly, in vain.
I am thinking to have an outdoor camera looking at chimneys and detect smoke. Then a program sends a text message for alerting. Most time, the image is pretty still and not a lot of variations. It shouldn't be a too hard problem for classification I imagine? I have little experience with Tensorflow or machine learning but I am a good programmer. So given some direction and some existing model, I hope I can get this working...
I know this sounds desperate, nevertheless, for a good deed. Please help.
For fire and smoke classification, you can check the following tutorial: https://www.pyimagesearch.com/2019/11/18/fire-and-smoke-detection-with-keras-and-deep-learning/.
PyImageSearch is a very good website for image processing, you can find there many articles which can help you (even deployment of neural networks on RaspberryPi and so on).
I'm new to the Machine-Learning (AI) technology. I'm developing a messenger app for Android/IOs where I would like to recommend the users based on the texts/word/conversation a product from a relative small product portfolio.
Example 1:
In case the user of the messenger writes a sentence including the words "vine", "dinner", "date" the AI should recommend a bottle of vine to the user.
Example 2:
In case the user of the app writes that he has drunk a good coffee this morning, the AI should recommend a mug to the user.
Example 3:
In case the user writes something about a cute boy she met last day, the AI should recommend a "teddy bear" to the user.
I'm a Software Developer since almost 20 year with experience in the development of C/C++/Java based application (Android and IOs apps) as well as some experience in Google Cloud Platform. The ML/AI technology is completely new to me. Okay, I know the basics (input data is needed to train the ML/AI system etc.), but I wonder If there is already a framework which could help me to develop such a system which solves the above described uses-case.
I would appreciate it, if you could give me some hints where and how to start.
Thank you and regards
It is definitely possible to implement such an application, in case you want to do it in Google Cloud you will need some understanding of Tensorflow.
First of all, I recommend to you to do the Machine Learning Crash Course, for a good introduction to Machine Learning and to start to familiarize yourself with TensorFlow. Afterwards I recommend to take a look into Tensorflow tutorials which will give you a more practical introduction to Tensorflow, and include various examples on building/training/testing models.
Once you are famirialized with Tensorflow, you can jump into learning how to run jobs in the Machine Learning engine, you can start by following the quickstart. The documentation includes detailed guides on how to use the ml-engine, plus multiple samples and tutorials.
Since I believe that your application would fall into the Recommender System type, here you can see an example model, in Google Cloud ML Engine, on how to recommend items to users based on his previous searches. In your case, you would have to build a model in order to recommend items to users based on his previous words in the sentence.
The second option, in case you don't want to go through the hassle of building a new model from scratch, would be to use the Google Cloud Natural Language API, which you can understand as pre-trained models using Google (incredibly big) data. In your case, I believe that the Content Classifying API would help you achieve what your application intends to do, however, the outputs (which you can see here) are limited to what the model was trained to do, and might not be specific enough for your application, however it is an easy solution and you can still profit of this API in order to extract labels/information and send it as input to another model.
I hope that these links provide you with some foundations on what is possible to do with Tensorflow in the ML Engine, and are useful to you.
There are a lot of programs that do parameter learning for Bayes nets. I am having a hard time finding libraries or tools that do (or try to do) structure learning. Specifically, one that uses an information theoretic approach, by looking at the information gain from adding an edge, or analyzing the cross entropy across Random Variables to determine if they have any relationships or are independent. This is not the core problem I am trying to work on, but learning structure is an important part of it. So finding an existing tool/library would help immensely.
Try the bnlearn library. It contains structure learning, parameter learning, inference and various well-known example datasets such as Sprinkler, Asia, Alarm.
Github documentation pages
Examples can be found here
Blog about detecting causal relationships can be found here.
I'm currently using a Processing Kinect library which supplies a depth map. I was wondering how I could take that and use it to create a 2D skeleton, if possible. Not looking for any code here, just a general process I could use to achieve those results.
Also, given that we've seen this in several of the Kinect games so far, would it be difficult to have multiple skeletons running at once?
Disclaimer: the reason why you still didn't get an answer for this question is probably because that's a current research problem. So I can't give you a direct answer but will try to help with some information and useful resources for this topic.
There are mainly 2 different approaches to create a skeleton from a depth map. The first one is to use machine learning, the second is purely algorithmic.
For the machine learning one, you'd need many samples of people doing a predetermined move, and use those samples to train your favorite learning algorithm. That's the approach that was taken and implemented by Microsoft in the XBox (source), it works really well BUT you need millions of samples to make it reliable... quite a drawback.
The "algorithmic" approach (understand without using a training set) can be done in many different ways and is a research problem. It's often based on modeling the possible body postures and trying to match that with the depth image received. That's the approach that was chosen by PrimeSense (the guys behind the kinect depth camera technology) for their skeleton tracking tool NITE.
The OpenKinect community maintains a wiki where they list some interesting research material about this topic. You might also be interested in this thread on the OpenNI mailing list.
If you're looking for an implementation of a skeleton tracking tool, PrimeSense released NITE (closed source), the one they made: it's part of the OpenNI framework. That's what's used in most of the videos you might have seen that involve skeleton tracking. I think it's able to handle up to 2 skeletons at the same time, but that requires confirmation.
The best solution is to use FAAST (http://projects.ict.usc.edu/mxr/faast/) which requires OpenNI. I have struggled to get OpenNI to work on my computer. I have not seen an approach yet using Code Laboratories' CL NUI.
An algorithmic approach is http://code.google.com/p/skeletonization/ but you may have a problem because your depthmap only represents surfaces and no closed objects.
I guess those who have worked in communities and social networks might have some experience in this.
I am trying to plot a graph of all the friendships that exists on my site and in doing so identify clusters of strongly interconnected users.
Does anyone have any experience in doing something like this? Also, does SQL Server 2008 BI have tools that allows for this type of modelling?
Thanks
Programming Collective Intelligence's chapter 5 is dedicated to optimization and network visualization. Using the modules available here and the snippet below, I could make the following image:
>>> import optimization
>>> import socialnetwork
>>> sol = optimization.annealingoptimize(socialnetwork.domain, socialnetwork.crosscount, step=50, cool=0.99)
>>> socialnetwork.drawnetwork(sol)
The advantages of this approach is that you can easily change the cost function, use different optimization algorithms, or use another library to view the solution.
Take a look at neato from the Graphviz command line tool suite. AS input it takes a so called .dot file. The format is straight forward you should just be able to iterate over all friendship relations in your system and write them into the file.
For inspiration, take a look at these social graphs from "Visual Complexity" collection.
Many visualizations have explanatory papers and articles mentioning graphing tools, libraries and algorithms used to obtain the images.
Examples from "Social Networks" category:
Your graph will be probably reasonably large, so GraphViz is a poor choice. It does a nice job for tiny graphs, but not for huge ones. I'd recommend that you try aiSee instead (here are some example graphs). It requires graphs to be specified in a simple human-readable format called GDL.
(source: aisee.com)
Sample social network http://www.aisee.com/graph_of_the_month/pubmed5.gif
(source: aisee.com)
For visualization, have a look at the Javascript Infovis Toolkit.
You might take a look at the Girvan-Newman algorithm, the output of which gives you an idea of community structure in the form of a dendrogram.
You should look at Mark Shepherd's SpringGraph which is a neat and sexy way of showing big graphs.
Please take a look at the prefuse visualization toolkit
Check out Wikipedia -- Social Network which does talk about social network analysis and graphing relations between users. I think the basic idea is you use a graph to map all the relations and then the more shared relations there are, the higher the interconnected relationships.