I have an FYP project (a social media app like Instagram) that requires me to create a simple recommendation system. I've trained my dataset on cosine similarity using Python, but I'm at a loss on what to do next. How can I integrate my trained ml model to React native or if there is a better and easier way to make a recommendation system?
I tried reading documentation and watching videos. But I still don't seem to be able to grasp some difficult concepts. I would greatly appreciate it if you could give me instructions or steps on what to learn after training my model. Or if I have to use some library or packages etc. [not sure if this is the appropriate forum for this inquiry]
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I have faster rcnn model that I trained and work on my google cloud instance with GPU ( train with google models API),
I want to run it on mobile, I found some GitHub that shows how to run SSDmobileNet but I could not found one that runs Faster-rcnn.
real time is not my concern for now.
I have iPhone 6, iOS 11.4
The model can be run with Metal, CoreML, tensorflow-lite...
but for POC I need it to run on mobile without train new network.
any help?
Thanks!
Faster R-CNN requires a number of custom layers that are not available in Metal, CoreML, etc. You will have to implement these custom layers yourself (or hire someone to implement them for you, wink wink).
I'm not sure if TF-lite will work. It only supports a limited number of operations on iOS, so chances are it won't have everything that Faster R-CNN needs. But that would be the first thing to try. If that doesn't work, I would try a Core ML model with custom layers.
See here info about custom layers in Core ML: http://machinethink.net/blog/coreml-custom-layers/
I'd like to make a machine learning with Tensorflow about sentiment analysis, I know it's possible to do machine learning with tensorflow but is it possible to make machine learning suited for sentiment analysis ? I know it's possible to do sentiment analysis with Convolutional Neuronal Network (Deep learning so) with Tensor flow but I'm looking for a solution that only uses Machine learning algorithm, not deep learning.
Would you know great tutorials to begin a GCP Machine Learning project ? Is it possible to begin a GCP Machine Learning Project without using google API or Tensorflow (just in "regular" python but which can be linked to other services like google big query, data prep) ?
Thank you very much for your answers :)
In general it wouldn't make much sense to use TensorFlow for non-deep learning solutions. You can always try scikit-learn for implementing other machine learning techniques with python, but I do not think that you could get far with sentiment analysis this way. For a nice introduction on sentiment analysis with TensorFlow I would suggest to go through this guide.
As for machine learning on google cloud platform: there is a number of APIs hosted in the platform which use pre-built models, including cloud natural language which has an analyzeSentiment method. Here you can find a python tutorial on how to do sentiment analysis using the python client library of the cloud natural language api.
If you still want to try and build your own model, you can train (with either scikit-learn or tensorflow), deploy and then use it for predictions on the cloud with the google cloud machine learning engine. As for tutorials, I would suggest that you visit the official documentations of any of the above products/services, you will find there comprehensive step-by-step guides for all levels.
I am able to do supervised learning for Facial Recognition with TensorFlow and especially after reading this great tutorial. I highly recommend the tutorial even if you don't intend on running the code.
Does anyone know of an open sourced library that does clustering of images based on facial recognition similar to Google Photos for instance? Or even a tutorial with sample code?
I found a good tutorial on autoencoders, so building my own is an option, but I wanted to check if there's something open sourced, or some tutorial that has code I maybe able to borrow?
Could somebody tell me whether it is possible to develop a chatbot using python ML frameworks such as tensorflow and deploy in Slack using Slack's apps?
As far as I have read we could develop some retrieval based model using node.js. But I'm looking for a generative model.
Anything to help me get started is much appreciated.
Thanks!
In order to use a generative model you have to use tensorflow sequence to sequence architecture . Here's the official TF tutorial from TF on sequence-to-sequence architecture .
So after training the model you can set this as an API which is easy. Then may be you can set it with the slack API for sure.
I heard Google has over 100 engineers working on Tensorflow. So, what does Google gain from distributing this expensive engine for free? Will Google later charge corporate Tensorflow users? ?
What is google’s plan for Tensorflow? will it be free forever? like a deep learning version of python?
Some people tell me it's to reduce new employee training time. If so, why did Baidu develop it's own deep learning library when that means they should spend extra time training it's employees who are used to using Tensorflow, how to use Baidu's deep learning library
Well if everyone is using tensorflow there are more contributions made to it by the public. That helps google for free.
Even if it doesn't become a product that they sell they can still make money from it.
The thing is they might be able to sell services around it like lessons. They can also sell hardware specifically optimized for tensorflow etc.
Almost every company had its own machine learning framework. The reason they open sourced them was because each of theirs were going to fall much behind if it wasn't open source. A prime example is how Torch was doing so great up until tensorflow became open source as well. (With google's backing and because it's in python instead of lua are a few reasons tf became more popular).