I'm working with the Backand with an Ionic mobile app that I'm developing. It's a data intensive app so I'm wanting to make sure doing my interactions most efficiently. Is there some parameters documented somewhere that tells me what are better ways to make calls? Like I'm assuming a Query using MySQL does requires more Compute Units than a REST GET call with filter but how much less? Thanks for any help as I haven't found any documentation on Backand Compute Units.
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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'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.
Is it possible in AWS XRay to create the sampling rule somehow that will sample all the calls for some service with duration greater than some value?
The only way right now to find the lagging sub-service is to sample 100% and then filter by service name and duration. This is pretty expensive having 10K+ segments per second.
AWS X-Ray dev here. Biased sampling on longer duration can skew your service graph statistics and cause false negatives. If you are ok with this data skew, depend on which X-Ray SDK you are using you might be able to achieve conditional sampling by making explicit sampling decisions on segment close. This would require you to override some certain part of the SDK behaviors.
We know this is a popular feature request and we are working on improving it. Please use our AWS X-Ray public forum https://forums.aws.amazon.com/forum.jspa?forumID=241&start=0 for latest feature launch or provide any additional comments.
We have trained a model using CNTK. We are building a service that is going to load this model and respond to requests to classify sentences. What is the best API to use regarding performance? We would prefer to build a C# service as in https://github.com/Microsoft/CNTK/tree/master/Examples/Evaluation/CSEvalClient but alternatively we are considering building a Python service that is going to load the model in python.
Do you have any recommendations towards one or the other approach? (regarding which API is faster, actively maintained or other parameters you can think of). The next step would be to set up an experiment measuring the performance of both API calls, but was wondering if there is some prior knowledge here that could help us decide.
Thank you
Both APIs are well developed/maintained. For text data I would go with the C# API.
In C# the main focus is fast and easy evaluation and for text loading the data is straightforward.
The Python API is good for development/training of models and at this time not much attention has been paid to evaluation. Furthermore, because of the wealth of packages loading data in exotic formats is easier in Python than C#.
The new C# Eval API based on CNTKLibrary will be available very soon (the first beta is probably next week). This API has functional parity with the C++ and Python API regarding evaluation.
This API supports using multiple threads to serve multiple evaluation requests in parallel, and even better, model parameters of the same loaded model is shared between these threads, which will significantly reduce memory usage in a service environment.
We have also a turorial about how to use Eval API in ASP.Net environment. It still refers to EvalDLL evaluation, but applies to the new C# API too. The document will be updated after the new C# API is released.
I'm learning KinectFusion, hope to use it to build a reconstruction application for 3D print.
Currently i'm confused by IntegrateFrame and ProcessFrame method of INuiFusionColorReconstruction.
ProcessFrame has one more parameter named maxAlignIterationCount, does it mean that ProcessFrame will do integrate for multiple times while IntegrateFrame only do integrate one time? Since ProcessFrame also only take one reference frame, what's the benefit to do integrate for multiple times?
From Microsoft's website:
"...the ProcessFrame function in the INuiFusionReconstruction interface... encapsulates the camera tracking (AlignDepthFloatToReconstruction) and the data integration step (IntegrateFrame) in one function call to be easier to call and more efficient as all processing takes place on the GPU without upload and readback for the individual steps as would occur when calling separately."
Kinect for Windows 1.7, 1.8