I'm thinking about creating an AI trying to play a game. However, it would take forever to learn by itself from 0. So I wonder if it would be possible to start the training with data from existing gameplay (from humans) and then switch to machine learning when the AI reach the point where it knows the basics.
If it's possible, is there an a way to do that with TensorFlow or should I do it from sratch?
If the game is not complicated (i.e. open world) then you may be interested in deep reinforcement learning (you mentioned tensorflow), which does not require existing gameplay. It will start playing the game itself and gradually gather experience and develop game-winning strategies based on it. Other than that, using supervised learning to create your model sounds difficult and needs more clarification in my opinion.
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I recently learned how to build ML models using sci-kit learn, tensorflow and keras and I applied my learnings to create a model to predict NBA games. My model ended up doing pretty well, so much so that I'd like to create a self-updating database that scrapes basketball-reference.com/leagues/ regularly to update game data so that anytime I want a prediction for an upcoming NBA game I can have the parameters ready without needing to do anything.
For this to work I need to know how to scrape basketball-reference.com/leagues/ and I thought I might try to use Selenium but it turned out to be more difficult than I realized. Most of my knowledge with programming/compsci is self-taught so I'm not sure where to turn or what to do to learn how to use Selenium.
Does anyone have any resources they may suggest or maybe some 'pre-requisites' that I should know before I try using Selenium?
I was thinking about checking out a course on Udemy!
A co-worker and I had an idea to create a little web game where a user enters a chunk of data about themselves and then the application would write for them to sound like them in certain structures. (Trying to leave the idea a little vague.) We are both new to ML and thought this could be a fun first dive.
We have a decent bit of background with PHP, JavaScript (FE and Node), Ruby a little bit of other languages, and have had interest in learning Python for ML. Curious if you can run a cost efficient ML library for text well with a web app, being most servers lack GPUs?
Perhaps you have to pay for one of the cloud based systems, but wanted to find the best entry point for this idea without racking up too much cost. (So far I have been reading about running Pytorch or TensorFlow, but it sounds like you lose a lot of efficiency running with CPUs.)
Thank you!
(My other thought is doing it via an iOS app and trying Apple's ML setup.)
It sounds like you are looking for something like Tensorflow JS
Yes, before jumping into training something with Deep Learning; (this might even be un-necessary for your purpose) try to build a nice and simple baseline for this.
Before Deep Learning (just a few yrs ago) people did similar tasks using n-gram feature based language models. https://web.stanford.edu/~jurafsky/slp3/3.pdf
Essentially you try to predict the next few words probabilistically given a small context(of n-words; typically n is small like 5 or 6)
This should be a lot of fun to work out and will certainly do quite well with a small amount of data. Also such a model will run blazingly fast; so you don't have to worry about GPUs and compute .
To improve on these results with Deep Learning, you'll need to collect a ton of data first; and it will be work to get it to be fast on a web based platform
I want to create a application which converts 2d-images/video into a 3d model. While researching on it i found out similar application like Trnio, Scann3D, Qlone,and few others(Though few of them provide poor output 3D model). I also find out about a technology launched by the microsoft research called mobileFusion which showed the same vision i was hoping for my application but these apps were non like that.
Creating a 3D modelling app is complex task, and achieving it to a high standard requires a lot of studying. To point you in the right direction, you most likely want to perform something called Structure-from-Motion(SfM) or Simultaneous Localization and Mapping (SLAM).
If you want to program this yourself OpenCV is a good place to start if you know C++ or Python. A typical pipeline involves; feature extraction and matching, camera pose estimation, triangulation and then optimised using a bundle adjustment. All pipelines for SfM and SLAM follow these general steps (with exceptions of course). All of these steps are possible is OpenCV although Googles Ceres Solver is an excellent open-source bundle adjustment. SfM generally goes onto dense matching which is where you get very dense point clouds which are good for creating meshes. A free open-source pipeline for this is OpenSfM. Another good source for tools is OpenMVG which has all of the tools you need to make a full pipeline.
SLAM is similar to SfM, however, has more of a focus on real-time application and less on absolute accuracy. Applications for this is more centred around robotics where a robot wants to know where it is relative to its environment, but it not so concerned on absolute accuracy. The top SLAM algorithms are ORB-SLAM and LSD-SLAM. Both are open-source and free for you to implement into your own software.
So really it depends what you want... SfM for high accuracy, SLAM for real-time. If you want a good 3D model I would recommend using existing algorithms as they are very good.
The best commercial software in my opinion... Agisoft Photoscan. If you can make anything half as good as this i'd be very impressed. To answer your question what resources will you require. In my opinion, python/c++ skills, the ability to google well and a spare time to read up on photogrammetry and SfM properly.
I'm looking into designing a software platform that will aid linguists and anthropologists in their study of previously unstudied languages. Statistics show that around 1,000 languages exist that have never been studied by a person outside of their respective speaker groups.
My goal is to utilize TensorFlow to make a platform that will allow linguists to study and document these languages more efficiently, and to help them create written systems for the ones that don't have a written system already. One of their current methods of accomplishing such a task is three-fold: 1) Record a native speaker conversing in the language, 2) Listening to that recording and trying to transcribe it into the IPA, 3) From the phonetics, analyzing the phonemics and phonotactics of the language to eventually create a written system for the speaker.
My proposed platform would cut that research time down from a minimum of a year to a maximum of six months. Before I start, I have some questions...
What would be required to train TensorFlow to transcribe live audio into the IPA? Has this already been done? and if so, how would I utilize a previous solution for this project? Is a project like this even possible with TensorFlow? if not, what would you recommend using instead?
My apologies for the magnitude of this question. I don't have much experience in the realm of machine learning, as I am just beginning the research process for this project. Any help is appreciated!
I guess I will take a first shot at answering this. Since the question is pretty general, my answer will have to be pretty general as well.
What would be required. At the very least you would have to have a large dataset of pre-transcribed data. Ideally a large amount of spoken language audio mapped to characters in the phonetic alphabet, so the system could learn the sound of individual characters rather than whole transcribed words. If such a dataset doesn't exist, a less granular dataset could be used, mapping single words to their transcriptions. Then you would need a model, that is the actual neural network architecture implemented in code. And lastly you would need some computing resources. This is not something you can train casually, you would either have to buy some time in a cloud based machine learning framework (like Google Cloud ML) or build a fairly expensive machine to train at home.
Has this been done? I don't know. I don't think so. There have been published papers reporting various degrees of success at training systems to transcribe speech. Here is one, for example, http://deeplearning.stanford.edu/lexfree/lexfree.pdf It seems that since the alphabet you want to transcribe to is specifically designed to capture the way words sound rather than just write down the words you might have more success at training such a model.
Is it possible with TensorFlow. Yes, most likely. TensorFlow is well suited for implementing most modern deep learning architectures. Unless you end up designing some really weird and very original model for this purpose, TensorFlow should work just fine.
Edit: after some thought in part 1, you would have to use a dataset mapping spoken words to their transcriptions, since I expect that the same sound pronounced separately would be different from when the same sound is used in a word.
This has actually been done, albeit in PyTorch, by a group at CMU: https://github.com/xinjli/allosaurus
I have been playing around with building some deep learning models in Python and now I have a couple of outcomes I would like to be able to show friends and family.
Unfortunately(?), most of my friends and family aren't really up to the task of installing any of the advanced frameworks that are more or less necessary to have when creating these networks, so I can't just send them my scripts in the present state and hope to have them run.
But then again, I have already created the nets, and just using the finished product is considerably less demanding than making it. We don't need advanced graph compilers or GPU compute powers for the show and tell. We just need the ability to make a few matrix multiplications.
"Just" being a weasel word, regrettably. What I would like to do is convert the the whole model (connectivity,functions and parameters) to a model expressed in e.g. regular Numpy (which, though not part of standard library, is both much easier to install and easier to bundle reliably with a script)
I fail to find any ready solutions to do this. (I find it difficult to pick specific keywords on it for a search engine). But it seems to me that I can't be the first guy who wants to use a ready-made deep learning model on a lower-spec machine operated by people who aren't necessarily inclined to spend months learning how to set the parameters in an artificial neural network.
Are there established ways of transferring a model from e.g. Theano to Numpy?
I'm not necessarily requesting those specific libraries. The main point is I want to go from a GPU-capable framework in the creation phase to one that is trivial to install or bundle in the usage phase, to alleviate or eliminate the threshold the dependencies create for users without extensive technical experience.
An interesting option for you would be to deploy your project to heroku, like explained on this page:
https://github.com/sugyan/tensorflow-mnist