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Image Classification in the Browser with Javascript

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Machine Learning has a reputation for demanding lots of data and powerful GPU computations. This leads many people to believe that building custom machine learning models for their specific dataset is impractical without a large investment of time and resources. In fact, you can leverage Transfer Learning on the web to train an accurate image classifier in less than a minute with just a few labeled images. Teaching a machine to classify images has a wide range of practical applications. You may have seen image classification at work in your photos app, automatically suggesting friends or locations for tagging.


Machine Learning In Node.js With TensorFlow.js - James Thomas

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TensorFlow.js is a new version of the popular open-source library which brings deep learning to JavaScript. Developers can now define, train, and run machine learning models using the high-level library API. Pre-trained models mean developers can now easily perform complex tasks like visual recognition, generating music or detecting human poses with just a few lines of JavaScript. Having started as a front-end library for web browsers, recent updates added experimental support for Node.js. This allows TensorFlow.js to be used in backend JavaScript applications without having to use Python.


Machine Learning For Front-End Developers With Tensorflow.js -- Smashing Magazine

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Charlie is currently a front-end developer at Atlassian in Sydney, a Mozilla Tech Speaker and Google Developer Expert in Web Technologies. Upgrade your inbox and get our editors' picks 2 a month -- delivered right into your inbox. Machine learning often feels like it belongs to the realm of data scientists and Python developers. However, over the past couple of years, open-source frameworks have been created to make it more accessible in different programming languages, including JavaScript. In this article, we will use Tensorflow.js to explore the different possibilities of using machine learning in the browser through a few example projects.


TensorFlow Mobile: Training and Deploying a Neural Network - inovex-Blog

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Smart Assistants, fancy image filters in Snapchat and apps like Prisma all have one thing in common--they are powered by Machine Learning. The use of Machine Learning in mobile apps is growing and new mobile apps are developed with Machine Learning based services as business models. In this blog series we want to give you hands-on advice on how you can train and deploy a convolutional neural network for image classification to a mobile app using the popular machine learning framework TensorFlow Mobile. Our task will be to classify images of houseplants which we have collected ourselves. You don't have to go and snap pictures of plants, however, because our approach is generic and can be used for training and deploying a convolutional neural network for image classification, independent of their subject.


Run image classification with Amazon SageMaker JumpStart

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Last year, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart hosts 196 computer vision models, 64 natural language processing (NLP) models, 18 pre-built end-to-end solutions, and 19 example notebooks to help you get started with using SageMaker. These models can be quickly deployed and are pre-trained open-source models from PyTorch Hub and TensorFlow Hub. These models solve common ML tasks such as image classification, object detection, text classification, sentence pair classification, and question answering. The example notebooks show you how to use the 17 SageMaker built-in algorithms and other features of SageMaker.