does anyone know if the google-cloud-speech recognition has a Medical Special Module ?
in advance, Thanks
Asaf Elkabets
M.A.R LTD
There is no special modules in Speech API. You can use Enhanced Models to get access to a special set of machine learning models for speech recognition or Cloud Machine Learning Engine to build your own model.
Related
I used random search and got the best hyper parameters for my model, can I pass that model to the AutoML?
Does AutoML do the random search for the best hyper parameters by itself? or is there something I need to pass?
I presume you're referring to Google Cloud AutoML. It is a cloud-based Machine Learning (ML) platform that suggests a no-code approach to building data-driven solutions. AutoML was designed to build custom models for both newcomers and experienced machine learning engineers.
For newcomers, you could use Vertex AI (fully automated) to build a ML model:
For experienced ML engineers, you could also use AutoML Tabular to build a custom model, with the ability to select a model and input the selected hyperparameters:
You can read more details from here
I'm developing a cross-platform app (iOS/Android/web) and am loving the fast, cheap on-device image labeling feature of ML Kit on mobile. Is there a way to replicate the behavior on the web? Are the ML Kit models available for re-use with a different ML library so it can be repurposed?
Unfortunately, it does not seem like ML Kit allows you to export models created using it, only import models. However, tensorflow.js lets you run TensorFlow models on the web. If you are looking for an easy way to create models there are several web-based programs which allow you to easily create ML models and export as TensorFlow Lite (which can be run in tensorflow.js or even hosted on Firebase). A couple I have heard of are: lobe.ai and ml5.js. Hope this helps.
Can we customize the Named Entity Recognition (NER) model in Azure ML Studio with a separate training dataset? What I want to do is to find out non-English names from a text. (Training dataset includes the set of names that going to use for training)
Unfortunately, this module's ability to perform NER with a custom set of entities is planned for the future, but not currently available.
If you're familiar with Python and willing to put in the extra footwork, you might consider using the Natural Language Toolkit (NLTK). Sujit Pal has a nice blog post and sample code describing the creation of a custom NER with that package. You may be able to train an NLTK NER model and apply it to your data of interest from within an Execute Python Script module on Azure ML.
I'm testing couple of IBM Watson APIs like the following:
Does Watson get smarter and learn more about my data the more I use it?
I read that Watson is getting smarter with more data it learns and processes. I'm not sure if this is only done behind the scenes by IBM Watson team, or if these API's as well allow an instance of Watson for example to be smarter with my specific application I'm developing.
If you mean that Watson is using the data you input into your instances, then no. Watson is IBM's, but your data is always yours.
By default, instances are isolated.
By smarter, they mean they have their very own instances of APIs which they train. Also they, improve algorithms behind the scenes.
It depends on your definition of learning. Is it offline learning or online learning? Do you refer to Watson learning from your corpus on the entire domain and use it later on, or just on your data.
It also depends which services you use, check out Retrieve and Rank or Natural Language Classifier for examples of services that learn from your data
Are there any machine learning packages that implement spiking neural networks? or any other stand-alone implementations of them that could get me started to work with?
A python library named Brian ought to be useful for you.
There's also what I believe is a programing language named NEURON, but Brian is fairly easy to learn, at least for the basics. It took me a while though to figure out how to do a couple small things, since its a really high level language or whatnot.
There are several other SNN platforms these days that allows you to run classification. I have worked with NeuCube (https://kedri.aut.ac.nz/R-and-D-Systems/neucube) which is a Matlab & Java-based SNN platform.
Also, check out Akida Development Environment (ADE) from Brainchip Inc (https://brainchipinc.com/). One of the best features of ADE is that it's APIs are based on tensorflow/keras structure and also supports CNN2SNN converter to use your deep learning models in SNN domain. SNN models developed using this platform can be deployed on their neuromorphic processor Akida.
I believe there are other platforms such as PyNN and Nengo (compatibility to run models on Loihi) within the SNN domain.
Here are links for brain simulator
https://github.com/brian-team/brian2
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2605403/
http://briansimulator.org/
You can install the Nengo Loihi library for deployment not only of spiking neural networks but also neuromorphic neural networks.
here's the link to their website: https://www.nengo.ai/nengo-loihi/v1.0.0/index.html
You can find on Kaggle an implementation of the ciphar10 dataset, locally loaded, using Nengo Loihi library. Here's the link:
https://www.kaggle.com/migueltoms/neuromorphic-ciphar-10-loihi-comparison-of-results