课程信息
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第 1 门课程(共 4 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.

完成时间大约为9 小时

建议:4 weeks, 4-5 hours/week...

英语(English)

字幕:英语(English), 西班牙语(Spanish), 俄语(Russian)

您将学到的内容有

  • Check

    Learn best practices for using TensorFlow, a popular open-source machine learning framework

  • Check

    Build a basic neural network in TensorFlow

  • Check

    Train a neural network for a computer vision application

  • Check

    Understand how to use convolutions to improve your neural network

您将获得的技能

Computer VisionTensorflowMachine Learning

第 1 门课程(共 4 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.

完成时间大约为9 小时

建议:4 weeks, 4-5 hours/week...

英语(English)

字幕:英语(English), 西班牙语(Spanish), 俄语(Russian)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 6 小时

A New Programming Paradigm

4 个视频 (总计 16 分钟), 5 个阅读材料, 3 个测验
4 个视频
A primer in machine learning3分钟
The ‘Hello World’ of neural networks5分钟
Working through ‘Hello World’ in TensorFlow and Python3分钟
5 个阅读材料
Before you begin: TensorFlow 2.0 and this course10分钟
From rules to data10分钟
Try it for yourself10分钟
Introduction to Google Colaboratory10分钟
Week 1 Resources10分钟
1 个练习
Week 1 Quiz
2
完成时间为 7 小时

Introduction to Computer Vision

7 个视频 (总计 15 分钟), 6 个阅读材料, 3 个测验
7 个视频
An Introduction to computer vision2分钟
Writing code to load training data2分钟
Coding a Computer Vision Neural Network2分钟
Walk through a Notebook for computer vision3分钟
Using Callbacks to control training1分钟
Walk through a notebook with Callbacks1分钟
6 个阅读材料
Exploring how to use data10分钟
The structure of Fashion MNIST data10分钟
See how it's done10分钟
Get hands-on with computer vision1小时
See how to implement Callbacks10分钟
Week 2 Resources10分钟
1 个练习
Week 2 Quiz
3
完成时间为 8 小时

Enhancing Vision with Convolutional Neural Networks

6 个视频 (总计 19 分钟), 6 个阅读材料, 3 个测验
6 个视频
What are convolutions and pooling?2分钟
Implementing convolutional layers1分钟
Implementing pooling layers4分钟
Improving the Fashion classifier with convolutions4分钟
Walking through convolutions3分钟
6 个阅读材料
Coding convolutions and pooling layers10分钟
Learn more about convolutions10分钟
Getting hands-on, your first ConvNet10分钟
Try it for yourself1小时
Experiment with filters and pools1小时
Week 3 Resources10分钟
1 个练习
Week 3 Quiz
4
完成时间为 9 小时

Using Real-world Images

9 个视频 (总计 27 分钟), 10 个阅读材料, 3 个测验
9 个视频
Understanding ImageGenerator4分钟
Defining a ConvNet to use complex images2分钟
Training the ConvNet with fit_generator2分钟
Walking through developing a ConvNet2分钟
Walking through training the ConvNet with fit_generator3分钟
Adding automatic validation to test accuracy4分钟
Exploring the impact of compressing images3分钟
A conversation with Andrew1分钟
10 个阅读材料
Explore an impactful, real-world solution10分钟
Designing the neural network10分钟
Train the ConvNet with ImageGenerator10分钟
Exploring the solution10分钟
Training the neural network10分钟
Experiment with the horse or human classifier1小时
Get hands-on and use validation30分钟
Get Hands-on with compacted images30分钟
Week 4 Resources10分钟
Wrap up10分钟
1 个练习
Week 4 Quiz
4.7
860 个审阅Chevron Right

42%

完成这些课程后已开始新的职业生涯

42%

通过此课程获得实实在在的工作福利

11%

加薪或升职

来自Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning的热门评论

创建者 ASMar 9th 2019

Good intro course, but google colab assignments need to be improved. And submitting a jupyter notebook was much more easier, why would I want to login to my google account to be a part of this course?

创建者 RDAug 14th 2019

Great course to get started with building Convolutional Neural Networks in Keras for building Image Classifiers. This is probably the best way to get beginners into Deep Learning for Computer Vision.

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Laurence Moroney

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Google Brain

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关于 TensorFlow in Practice 专项课程

Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework. In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry! AI is already transforming industries across the world. After finishing this Specialization, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. Courses 1-3 are available now, with Course 4 launching in July....
TensorFlow in Practice

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