pre-trained model


Getting started with Machine Learning on MCUs with TensorFlow – Particle Blog

#artificialintelligence

Over the past several months, I've been working with engineers at Google, Adafruit, and our own stellar team at Particle to make machine learning possible on Particle devices. Today I'm happy to report that, with the help of TensorFlow Lite for Microcontrollers, it's now possible to perform fast ML inferencing -- that is, making predictions on live data using pre-trained models -- with Particle devices. In this post, I'll show you how to get started using TensorFlow Lite in your own projects. To follow along, you'll need a Particle Photon or Electron or one of the new Gen 3 devices like an Argon, Boron, or Xenon. You'll also need a USB cable or other power source, but that's it.


Machine learning ops to lead AI in 2020

#artificialintelligence

Last year, we predicted that 2019 was going to be the year of the pre-trained model and improved third party datasets. For 2020 we're going to take this prediction one step further and say that these pre-trained models offered by third parties will become a larger percentage of the overall model usage. Known as model-as-a-service, we'll see entities set up specifically to offer models for usage on a per-consumption, subscription or license basis. This means we'll start to see more model marketplaces and model-as-a-service embedded in cloud offerings. Currently machine learning is, for the most part, limited to companies that have large data sets and large teams.


Fighting Overfitting in Deep Learning

#artificialintelligence

While training the model, we want to get the best possible result according to the chosen metric. And at the same time we want to keep a similar result on the new data. The cruel truth is that we can't get 100% accuracy. And even if we did, the result is still not without errors. There are simply too few test situations to find them. You may ask, what is the matter?


Let's build our own Image Classification Machine Learning on the Web with Tensorflow Js, MobileNet…

#artificialintelligence

Now, as we can see that there were many problems and use cases that includes Image Classification as part of the solution with various situation and condition, while there were many people still not used to with building solution in this kind of field like Artificial Intelligence, Machine Learning and even Deep Learning. Basically there were many types of Machine Learning Implementation such as 1. So in that case, since most problems and use cases fit perfectly with a solution that is available on the web, with a simple, lightweight and straight to the point solution where all people can enjoy, so in here i will show you how to build this Machine Learning on the Web! Tech stack that we will use in here are: 1. Tensorflow Js (Of course:D) https://www.tensorflow.org/js/ A machine learning framework that can be used on the client side on the web. A Basic Machine Learning model that has been created for us for Image Classification.


Fighting Overfitting in Deep Learning - KDnuggets

#artificialintelligence

While training the model, we want to get the best possible result according to the chosen metric. And at the same time we want to keep a similar result on the new data. The cruel truth is that we can't get 100% accuracy. And even if we did, the result is still not without errors. There are simply too few test situations to find them. You may ask, what is the matter?


Google's BERT changing the NLP Landscape

#artificialintelligence

We write a lot about open problems in Natural Language Processing. We complain a lot when working on NLP projects. We pick on inaccuracies and blatant errors of different models. But what we need to admit is that NLP has already changed and new models have solved the problems that may still linger in our memory. One of such drastic developments is the launch of Google's Bidirectional Encoder Representations from Transformers, or BERT model -- the model that is called the best NLP model ever based on its superior performance over a wide variety of tasks.


An Intro to TensorFlow.js: Machine Learning made Accessible in JavaScript.

#artificialintelligence

If you haven't heard of TensorFlow.js yet, let me introduce you! It aims to allow programmers to create and run machine learning models in JavasScript easily and quickly! It can be used in the browser or sever-side in Node.js. The library provides pre-trained Machine Learning models you can implement without little to no previous knowledge of machine learning. A machine learning model is a function with learnable parameters that maps an input to a desired output.


airsplay/lxmert

#artificialintelligence

Slides of our EMNLP 2019 talk are avialable here. All the results in the table are produced exactly with this code base. Since VQA and GQA test servers only allow limited number of'Test-Standard' submissions, we use our remaining submission entry from the VQA/GQA challenges 2019 to get these results. For NLVR2, we only test once on the unpublished test set (test-U). We use this code (with model ensemble) to participate in VQA 2019 and GQA 2019 challenge in May 2019.


How to Use BERT to Generate Meta Descriptions at Scale

#artificialintelligence

For now, the solution appears to be to retrain the model using datasets in your domain. This is a great primer on classical text summarization. I covered text summarization a couple of months ago during a DeepCrawl webinar. At that time the PreSumm researchers released an earlier version of their work focused only on extractive text summarization.


Artificial Intelligence for learning Sign Language

#artificialintelligence

This story began in Madrid, Spain. The winter was comming, and a team of four young enthusiasts started a project. The initial idea was to create an app to learn Sign Language, not only because it is an interesting aspect of our society, but for those 34 million children with disabling hearing loss that need to learn it to communicate. The beauty of technology is that it can be used to help others too. We aimed to do exactly that.