課程信息
669,674 次近期查看

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

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

完成時間大約為7 小時

建議:4 weeks, 4-5 hours/week...

英語(English)

字幕:英語(English)

您將學到的內容有

  • 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

您將獲得的技能

Computer VisionTensorflowMachine Learning

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

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

完成時間大約為7 小時

建議:4 weeks, 4-5 hours/week...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 3 小時

A New Programming Paradigm

Welcome to this course on going from Basics to Mastery of TensorFlow. We're excited you're here! In week 1 you'll get a soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, and you'll pick the rest up as you go along. To get started, check out the first video, a conversation between Andrew and Laurence that sets the theme for what you'll study......
4 個視頻 (總計 16 分鐘), 5 個閱讀材料, 3 個測驗
4 個視頻
A primer in machine learning3分鐘
The ‘Hello World’ of neural networks5分鐘
Working through ‘Hello World’ in TensorFlow and Python3分鐘
5 個閱讀材料
Learner Support10分鐘
From rules to data10分鐘
Try it for yourself10分鐘
Introduction to Google Colaboratory10分鐘
Week 1 Resources10分鐘
1 個練習
Week 1 Quiz
2
完成時間為 4 小時

Introduction to Computer Vision

Welcome to week 2 of the course! In week 1 you learned all about how Machine Learning and Deep Learning is a new programming paradigm. This week you’re going to take that to the next level by beginning to solve problems of computer vision with just a few lines of code! Check out this conversation between Laurence and Andrew where they discuss it and introduce you to Computer Vision! ...
7 個視頻 (總計 15 分鐘), 6 個閱讀材料, 3 個測驗
7 個視頻
An Introduction to computer vision2分鐘
Writing code to load training data2分鐘
Coding a Computer Vision Neural Network2分鐘
Walk through a Notebook for computer vision3分鐘
Using Callbacks to control training1分鐘
Walk through a notebook with Callbacks1分鐘
6 個閱讀材料
Exploring how to use data10分鐘
The structure of Fashion MNIST data10分鐘
See how it's done10分鐘
Get hands-on with computer vision
See how to implement Callbacks10分鐘
Week 2 Resources10分鐘
1 個練習
Week 2 Quiz
3
完成時間為 5 小時

Enhancing Vision with Convolutional Neural Networks

Welcome to week 3! In week 2 you saw a basic Neural Network for Computer Vision. It did the job nicely, but it was a little naive in its approach. This week we’ll see how to make it better, as discussed by Laurence and Andrew here. ...
6 個視頻 (總計 19 分鐘), 6 個閱讀材料, 3 個測驗
6 個視頻
What are convolutions and pooling?2分鐘
Implementing convolutional layers1分鐘
Implementing pooling layers4分鐘
Improving the Fashion classifier with convolutions4分鐘
Walking through convolutions3分鐘
6 個閱讀材料
Coding convolutions and pooling layers10分鐘
Learn more about convolutions10分鐘
Getting hands-on, your first ConvNet10分鐘
Try it for yourself
Experiment with filters and pools
Week 3 Resources10分鐘
1 個練習
Week 3 Quiz
4
完成時間為 6 小時

Using Real-world Images

Last week you saw how to improve the results from your deep neural network using convolutions. It was a good start, but the data you used was very basic. What happens when your images are larger, or if the features aren’t always in the same place? Andrew and Laurence discuss this to prepare you for what you’ll learn this week: handling complex images!...
9 個視頻 (總計 27 分鐘), 10 個閱讀材料, 3 個測驗
9 個視頻
Understanding ImageGenerator4分鐘
Defining a ConvNet to use complex images2分鐘
Training the ConvNet with fit_generator2分鐘
Walking through developing a ConvNet2分鐘
Walking through training the ConvNet with fit_generator3分鐘
Adding automatic validation to test accuracy4分鐘
Exploring the impact of compressing images3分鐘
Outro: A conversation with Andrew1分鐘
10 個閱讀材料
Explore an impactful, real-world solution10分鐘
Designing the neural network10分鐘
Train the ConvNet with ImageGenerator10分鐘
Exploring the solution10分鐘
Training the neural network10分鐘
Experiment with the horse or human classifier
Get hands-on and use validation30分鐘
Get Hands-on with compacted images30分鐘
Week 4 Resources10分鐘
Outro10分鐘
1 個練習
Week 4 Quiz
4.6
242 個審閱Chevron Right

46%

完成這些課程後已開始新的職業生涯

42%

通過此課程獲得實實在在的工作福利

熱門審閱

創建者 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?

創建者 HWMay 20th 2019

The course demystified simple computer vision classification use-cases by leveraging TensorFlow. This is a great follow-on course to Andrew Ng's 11-week Stanford Machine Learning course.

講師

Avatar

Laurence Moroney

AI Advocate
Google Brain

關於 deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

常見問題

  • 注册以便获得证书后,您将有权访问所有视频、测验和编程作业(如果适用)。只有在您的班次开课之后,才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程,可能无法访问某些作业。

  • 您购买证书后,将有权访问所有课程材料,包括评分作业。完成课程后,您的电子课程证书将添加到您的成就页中,您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容,可以免费旁听课程。

還有其他問題嗎?請訪問 學生幫助中心