Hyperparameter Tuning with Neural Network Intelligence

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在此指导项目中,您将:

Create and run hyperparameter tuning experiments using NNI

Clock2 hours
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

In this 2-hour long guided project, we will learn the basics of using Microsoft's Neural Network Intelligence (NNI) toolkit and will use it to run a Hyperparameter tuning experiment on a Neural Network. NNI is an open source, AutoML toolkit created by Microsoft which can help machine learning practitioners automate Feature engineering, Hyperparameter tuning, Neural Architecture search and Model compression. In this guided project, we are going to take a look at using NNI to perform hyperparameter tuning. Please note that we are going to learn to use the NNI toolkit for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves. We will use the popular MNIST dataset and train a simple Neural Network to learn to classify images of hand-written digits from the dataset. Once a basic script is in place, we will use the NNI toolkit to run a hyperparameter tuning experiment to find optimal values for batch size, learning rate, choice of activation function for the hidden layer, number of hidden units for the hidden layer, and dropout rate for the dropout layer. To be able to complete this project successfully, you should be familiar with the Python programming language. You should also be familiar with Neural Networks, TensorFlow and Keras. Note: 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.

您要培养的技能

Deep LearningArtificial Neural NetworkMachine Learningautomlhyperparameter tuning

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Introduction

  2. Rhyme Interface

  3. Load Data

  4. Create Model

  5. Model Training

  6. Hyperparameter Search Space

  7. Creating and Running the Experiment

  8. Final Results

指导项目工作原理

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在分屏视频中,您的授课教师会为您提供分步指导

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