课程信息
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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7 小时

建议:4 weeks, 4-5 hours/week...

英语(English)

字幕:英语(English)

您将学到的内容有

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

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    Build a basic neural network in TensorFlow

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    Train a neural network for a computer vision application

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    Understand how to use convolutions to improve your neural network

您将获得的技能

Computer VisionTensorflowMachine Learning

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7 小时

建议:4 weeks, 4-5 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 3 小时

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 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
Learner Support10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 4 小时

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 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 vision
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 5 小时

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 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 yourself
Experiment with filters and pools
Week 3 Resources10分钟
1 个练习
Week 3 Quiz
4
完成时间为 6 小时

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 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
Outro: 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 classifier
Get hands-on and use validation30分钟
Get Hands-on with compacted images30分钟
Week 4 Resources10分钟
Outro10分钟
1 个练习
Week 4 Quiz
4.6
244 个审阅Chevron Right

46%

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

热门审阅

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 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.

讲师

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Laurence Moroney

AI Advocate
Google Brain

关于 deeplearning.ai

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

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