https://www.youtube.com/channel/UCXe-BTXAnQ9VaQQZnlC608A
This guy made machine learning script and teach machine
how to play Super Mario and complete each level.
There's FAQ document in the description of every his video
that he's using LUA to make this script but I don't even know where
to start and can't find any tutorial on youtube how to make something like this
My goal is make machine learning script for other games and see
the machine learning how to play and complete various levels
Could you please guide me where I can start and what I should learn to make script like this?
and I prefer programming language easier to learn if there is other option.
Consider the following:
Are you a beginner programmer? If so, diving into machine learning will prove to be frustrating. You need a solid foundation first in programming principles.
The next level would be to use a machine learning library. The core ML algorithms are then already written and optimized and you just need to learn how to integrate them into your own programs. Don't be fooled though - to do this well still requires a solid understanding of ML principles. There are existing libraries for Lua and Python. That is a good place to start.
I would consider looking at Andrew Ng's introductory material (book and videos) on ML. Warning! He knows what he is talking about and does offer up quite a bit of advanced material as well. Don't start with that.
There are excellent, advanced books on the subject that explain the mathematical principles behind the apparent magic of ML. If you already did all of the above and are still sticking with it, then this would be the way to go next. You could work on your own implementation of ML and apply that to any domain.
I'm gonna guess that you are at the first bullet point still. Be patient. Learn to program well. Get exposure to ML ideas. Plan on that video game ML application some years from now.
Related
I need advice on which libraries and game engines should I use for a ml project
my goal is to create machine learning model for pruning the trees. I believe I have to create a game with generic tree model with some randomness then create reinforcement learning model and train ml model inside the game.ML model must have ability to first find the branch which must be cut and then find a path to move robotic arm near to that branch to cut it. I have experience in c++ and java but I prefer c++ , could you give me advise which library should I use for ML and which language and game engine should I use for creating game? I have a little experience in opengl. If it doesn't make any difference my prefered language is c++ but I know that I should use right tool for right job and python is leader in ML so if it will save a time and energy I have nothing against learning python.
My recommendation is to learn and use Python for your ML project. Though there is some work in R, for your future in ML, your best bet is to learn and use Python. The community is great, and there are many frameworks that can work out-of-the-box.
After a quick search, I did find a framework called robotframework, that is pretty highly starred on GitHub here: https://github.com/robotframework/robotframework. I will say though, however, that I am not personally familiar with using this framework. But it may be helpful to you.
In terms of tree-based algorithms, you might want to start exploring with XGBoost. It can be found here: https://github.com/dmlc/xgboost.
I have had an infatuation with a certain concept regarding machine learning that Sethbling proved with his Mar.io program: https://youtu.be/qv6UVOQ0F44
I have a decent amount of logical programming experience in a number of different languages and have read around a lot about machine learning and neural networking.
What I'm looking for is a good set of references that could teach me how to apply neural networks in code, rather than just as a mathematical teaching like most of what I have seen thus far.
Thanks in advance!
Sentdex (https://www.youtube.com/user/sentdex) has incredible tutorials on Youtube and walks through teaching a model to play GTA.
It may seem daunting at first, but the rewards of overcoming such a challenging task will be worth it.
You might want to check out the JavaScript library Neataptic to check out how they implemented neural networks in Agar.IO for example.
You might also want to check out the NeuroEvolution of Augmenting Topologies paper for a basic understanding of neuro-evolution.
Recently, we planned to build a system for image processing to extract info from images. At present we are using AWS Rekognition to do that. But, in some cases, we are not getting accurate information from AWS. So, we've planned to build our own custom one.
We've 4/5 months to do that. At least a POC version. Also, we've planned to use Tensorflow for that. We all have no prior experience about Machine Learning & Deep Learning but already have 5/6yrs of experience on Computer Programming by using different languages.
Currently, I'm studying ML from a course of Udemy & my approach to solve this problem is...
Learn Machine Learning(ML)
Learn Deep Learning(DL)
Above ML & DL maybe I'll be ready to understand the whole thing & can able to build a system for Image Processing.
In abstract what I've understood is, I've to write one Deep Learning program in Python by using Tensorflow. By using that Program I've to build a Model. Then I've to train that Model by using some training data. Then, when my Model achieves a certain level of accuracy I'll use some test data.
Now, there some places at where I've bit confused & here are my questions regarding that confusion...
I know tensorflow is a library but at some places, it's also mentioned as a system. So, is it really a library(piece of code) only & something more than that?
I got some Image Processing Python code in Tensorflow tutorial section (https://www.tensorflow.org/tutorials/image_recognition). We've tested that code & it's working exactly the way AWS Recognition service work. So, here my doubt is... can I use this Python code as it is in our production work?
After train a model with some training data does those training data get part of the whole system or Machine Learning Model extract some META info from those training data & keep with itself rather whole raw training data(in my case it'll be raw images).
Can I do all these ML+DL programmings over my Linux System? It has Pentium 4 with 8GB RAM.
Also, want to know... the approach which I've mentioned to build a solution for my problem is sufficient or I need to do something else also.
Need some guidance to clear out all these confusion.
Thanks
1 : tensor-flow is like anything else we have been worked with (like Numpy ) but only difference is we have to first defined what we want to use the use it , every thing in tensor-flow are running into a computational graph and evaluating every thing in that graph require a Session , we could call it library because it just piece of code and have interface in python , and system because of all those mechanism it uses
2 :
can I use this Python code as it is in our production work? Why not !
3:
yes you could do that with your system , but the main advantage of tensor-flow and theano , .. the tool like those is that you could run your code on GPU it a more faster way than on CPU because the GPU could handle a lot more matrix multiplication and stuff like that
4:
you know you don't have to learn all the machine learning stuff to built a image recognition system , it may be take years for you to understand whats going on there , Udemy course is very good source but you I highly recommend you to see the machine learning courses of coursera , there is to courses there about machine learning : the great Andrew NG course and Emily fox course , the first one is more theoretical than practical , but second on is more practical ,
and about the Deep learning , there is nothing fancy about Deep learning and it's just a method in machine learning , after you gain some experience in machine learning and understood some basic or you could do it right know , go to fast.ai , it has a really good course about deep learning for coder and it's also free
I hope this will help you
I am new to the field of machine learning, I am planning to use python as the programing language for implementing algorithms and Java for system architecture.
As far as I understand, machine learning is more about modeling data specific to the domain, visualize the data, and choose appropriate models & parameters. Implementing the models/algorithms is the last and relatively easy step.
Matlab seems to have everything for machine learning but it is too expensive and requires to learn a new language.
What tools other than programming language do I need in general for machine learning for enterprise projects? things like data modeling, visualization,etc
After a couple of years of trial and error, I would suggest you to go directly with python, possibly with scikit-learn or tensorflow (if you want to go hardcore :).
I also tried R in the past, and while it is a very valid language it has some limitations: It is single threaded by default, and although there are solutions for that, they are non as clean as python.
Also, python seems to be THE language for machine learning, it is easy to learn, and fast (depending on the interpreter implementation of course), also there is huuuuuuge support for it, lots of tutorials, documentation and, more important, libraries are actively develop and supported.
Finally, i recommend you to consider spyder as a good IDE for data science, I also tried Rodeo, but it does not seem as mature and stable as spyder.
Hope this helps.
I'd like to programatically do some signal processing on a live sound feed.
Specifically I'd like to be able to isolate certain bands of frequencies and play around with phase shifting.
I've not worked in this area before from a purely software perspective and a quick google search turned up very little useful information.
Does anyone know of any good information resources for this topic area?
Matlab is a good starting point. It has the necessary toolboxes and functions that will allow you to capture audio signals, run different kind of filters over them and write them to wav files. The UI is easy to navigate through and it's simple enough to generate plots and visualize results.
http://www.mathworks.com/products/signal/
If, however, you're looking to develop real-world applications, then Python can come in handy. They have toolkits like SciPy, Numpy, Audiolab that offer the same functions as Matlab does.
http://www.scipy.org
Link
http://scikits.appspot.com/audiolab
In a nutshell, Matlab is good for testing ideas and prototyping, Python is good for testing as well as real-world application development. And Python is free. Matlab might cost you if you're not a student anymore.
http://www.dspguide.com/
This is a super excellent reference on digital signal processing techniques in general. It's not a programming guide, per se, but covers the techniques and the theory clearly and simply, and provides pseudocode and examples so that you can implement in the language of your choice. You'll be hard up to find a more complete reference, and you can download it for free online!