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Building a propensity model for financial services on GCP Solutions Google Cloud

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In the following steps you create an AI Platform Notebooks instance. In the GCP Console, go to the AI Platform Notebook instances page. On the menu bar, click New Instance add, and then select the TensorFlow 1.x framework. It takes a few minutes for AI Platform Notebooks to create the new instance. Now that you have an AI Platform Notebooks instance, you can download the notebook file for this tutorial.


Best practices for implementing machine learning on Google Cloud

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Use BigQuery to process tabular data. Use Dataflow to process unstructured data. Use managed datasets to link data to your models. The recommended approach for processing your data depends on the framework and data types you're using. This section provides high-level recommendations for common scenarios. For general recommendations on data engineering and feature engineering for ML, see Data preprocessing for machine learning: options and recommendations and Data preprocessing for machine learning using TensorFlow Transform. If you're using TensorFlow for model development, use TensorFlow Extended to prepare your data for training. TensorFlow Transform is the TensorFlow component that enables defining and executing a preprocessing function to transform your data.


Top Databases Supporting in-Database Machine Learning - ELE Times

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In my August 2020 article, "How to choose a cloud Machine Learning platform," my first guideline for choosing a platform was, "Be close to your data." Keeping the code near the data is necessary to keep the latency low, since the speed of light limits transmission speeds. After all, machine learning -- especially deep learning -- tends to go through all your data multiple times (each time through is called an epoch). I said at the time that the ideal case for very large data sets is to build the model where the data already resides, so that no mass data transmission is needed. Several databases support that to a limited extent.


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.


Google Cloud AutoML Vision for Medical Image Classification

#artificialintelligence

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.