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Solve a basic image classification problem with neural networks
Build, train, and evaluate a neural network model with Keras using R
In this 45-min guided project, you will learn the basics of using the Keras interface to R with Tensorflow as its backend to solve an image classification problem. By the time you complete this project, you will have used the R programming language to build, train, and evaluate a neural network model to classify images of clothing items into categories such as t-shirts, trousers, and sneakers. We will be training the deep learning based image classification model on the Fashion MNIST dataset which contains 70000 grayscale images of clothes across 10 categories. In order to be successful in this project, you should be familiar with R programming, and basics of neural networks. 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.
Deep Learning
Artificial Neural Network
Machine Learning
Tensorflow
keras
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：
Project Overview and Import Libraries
Import the Fashion MNIST Dataset
Data Exploration
Preprocess the Data
Build the Model
Compile the Model
Train and Evaluate the Model
Make Predictions on Test Data
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
由 JT 提供
May 10, 2020Very nice, concise, clean and to the point project.
由 AR 提供
May 14, 2020Awesome live session for practice using cloud desktop
由 AG 提供
Jun 16, 2020I like the way we got involved into practice by setting goals which are a bit challenging yet we want to achieve successfully.
由 VP 提供
May 30, 2020excellent way of getting a sense of deep learning.
购买指导项目后，您将获得完成指导项目所需的一切，包括通过 Web 浏览器访问云桌面工作空间，工作空间中包含您需要了解的文件和软件，以及特定领域的专家提供的分步视频说明。
由于您的工作空间包含适合笔记本电脑或台式计算机使用的云桌面，因此指导项目不在移动设备上提供。
指导项目授课教师是特定领域的专家，他们在项目的技能、工具或领域方面经验丰富，并且热衷于分享自己的知识以影响全球数百万的学生。
您可以从指导项目中下载并保留您创建的任何文件。为此，您可以在访问云桌面时使用‘文件浏览器’功能。
指导项目不符合退款条件。请查看我们完整的退款政策。
指导项目不提供助学金。
指导项目不支持旁听。
您可在页面顶部点按此指导项目的经验级别，查看任何知识先决条件。对于指导项目的每个级别，您的授课教师会逐步为您提供指导。
是，您可以在浏览器的云桌面中获得完成指导项目所需的一切。
您可以直接在浏览器中于分屏环境下完成任务，以此从做中学。在屏幕的左侧，您将在工作空间中完成任务。在屏幕的右侧，您将看到有授课教师逐步指导您完成项目。
还有其他问题吗？请访问 学生帮助中心。
