Hyperparameter Tuning with Keras Tuner

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Coursera Project Network
在此免费指导项目中,您将:

Create and run hyperparameter tuning experiments using Keras Tuner

Create and use Custom Keras Tuners

在面试中展现此实践经验

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 use Keras Tuner to find optimal hyperparamters for a Keras model. Keras Tuner is an open source package for Keras which can help machine learning practitioners automate Hyperparameter tuning tasks for their Keras models. The concepts learned in this project will apply across a variety of model architectures and problem scenarios. Please note that we are going to learn to use Keras Tuner for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves. At the time of recording this project, Keras Tuner has a few tuning algorithms including Random Search, Bayesian Optimization and HyperBand. In order to complete this project successfully, you will need prior programming experience in Python. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, and optimization algorithms like gradient descent but want to understand how to use Keras Tuner to start optimizing hyperparameters for training their Keras models. You should also be familiar with the Keras API. 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.

必备条件

Prior programming experience in Python. Conceptual understanding of Neural Networks. Prior experience with TensorFlow and Keras.

您要培养的技能

  • Deep Learning
  • Machine Learning
  • Hyperparameter Optimization
  • hyperparameter tuning
  • keras

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. Installing Keras Tuner and Downloading the Data

  3. Creating the Model

  4. Hyperparameters

  5. Keras Tuner

  6. Training Results

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

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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