Google Cloud AutoML Vision for Medical Image Classification

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The concepts of neural architecture search and transfer learning are used under the hood to find the best network architecture and the optimal hyperparameter configuration that minimizes the loss function of the model. This article uses Google Cloud AutoML Vision to develop an end-to-end medical image classification model for Pneumonia Detection using Chest X-Ray Images. The dataset is hosted on Kaggle and can be accessed at Chest X-Ray Images (Pneumonia). Go to the cloud console: https://cloud.google.com/ Setup Project APIs, permissions and Cloud Storage bucket to store the image files for modeling and other assets.


4 Awesome Ways Of Loading ML Data In Google Colab

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Google Colaboratory or Colab has been one of the favorite development environment for ML beginners as well as researchers. It is a cloud-based Jupyter notebook do there have to be some awesome ways of loading machine learning data right from your local machine to the Cloud. We'll be discussing some methods which would avoid you to click the "Upload" button directly! If you are working on a project which has its own dataset like any object detection model, classification models etc. then we will like to pull the dataset from GitHub directly. If the dataset is in an archive ( .zip or .tar


Crowdsourcing ML training data with the AutoML API and Firebase

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Want to build an ML model but don't have enough training data? In this post I'll show you how I built an ML pipeline that gathers labeled, crowdsourced training data, uploads it to an AutoML dataset, and then trains a model. I'll be showing an image classification model using AutoML Vision in this example but the same pipeline could easily be adapted to AutoML Natural Language. Here's an overview of how it works: Want to jump to the code? The full example is available on GitHub.


A Marketer's Guide to Kaggle for Analytics and Data Science

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Kaggle, the Google-acquired data science platform, started as a virtual meeting point for machine-learning geeks to compete on predictive accuracy scores. It evolved into a Swiss Army knife for data science and analytics--one that can help data professionals, including data-driven marketers, elevate their analytics game. This is, of course, just a partial list. This post focuses on these and other marketing-friendly use cases for Kaggle. It became known as a platform for hosting machine-learning competitions.


Cloud AutoML: Making AI accessible to every business

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When we both joined Google Cloud just over a year ago, we embarked on a mission to democratize AI. Our goal was to lower the barrier of entry and make AI available to the largest possible community of developers, researchers and businesses.