Deep Learning Inference with Azure ML Studio

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Deep Learning Inference with Azure ML Studio Offered by Coursera Project Network. In this project-based course, you will use the Multiclass Neural Network module in Azure Machine Learning Studio to train …

In this project-based course, you will use the Multiclass Neural Network module in Azure Machine Learning Studio to train a neural network to recognize handwritten digits. Microsoft Azure Machine Learning Studio is a drag-and-drop tool you can use to rapidly build and deploy machine learning models on Azure. The data used in this course is the popular MNIST data set consisting of 70,000 grayscale images of hand-written digits. You are going to deploy the trained neural network model as an Azure Web service. Azure Web Services provide an interface between an application and a Machine Learning Studio workflow scoring model. You will write a Python application to use the Batch Execution Service and predict the class labels of handwritten digits. This is the third course in this series on building machine learning applications using Azure Machine Learning Studio. I highly encourage you to take the first course before proceeding. It has instructions on how to set up your Azure ML account with $200 worth of free

with Python, Jupyter, and scikit-learn pre-installed. Notes:

– You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.

– This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

Introduction and Overview

Data Preparation

Train and Evaluate Multiclass Neural Network

Create and Deploy Predictive Web Service

Inference Using the Web Service API

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