If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
提供方
TensorFlow in Practice 专项课程deeplearning.ai课程信息
582,702 次近期查看
学生职业成果
13%
通过此课程获得实实在在的工作福利
10%
加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 4 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.
完成时间大约为26 小时
英语（English）
字幕：英语（English）, 俄语（Russian）, 日语
您将学到的内容有
Handle real-world image data
Plot loss and accuracy
Explore strategies to prevent overfitting, including augmentation and dropout
Learn transfer learning and how learned features can be extracted from models
您将获得的技能
Inductive TransferAugmentationDropoutsMachine LearningTensorflow
学生职业成果
13%
通过此课程获得实实在在的工作福利
10%
加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 4 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.
完成时间大约为26 小时
英语（English）
字幕：英语（English）, 俄语（Russian）, 日语
提供方
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
教学大纲 - 您将从这门课程中学到什么
完成时间为 7 小时
Exploring a Larger Dataset
In the first course in this specialization, you had an introduction to TensorFlow, and how, with its high level APIs you could do basic image classification, an you learned a little bit about Convolutional Neural Networks (ConvNets). In this course you'll go deeper into using ConvNets will real-world data, and learn about techniques that you can use to improve your ConvNet performance, particularly when doing image classification!
完成时间为 7 小时
8 个视频 （总计 18 分钟）, 5 个阅读材料, 3 个测验
8 个视频
A conversation with Andrew Ng1分钟
Training with the cats vs. dogs dataset2分钟
Working through the notebook4分钟
Fixing through cropping49
Visualizing the effect of the convolutions1分钟
Looking at accuracy and loss1分钟
Week 1 Wrap up33
5 个阅读材料
Before you Begin: TensorFlow 2.0 and this Course10分钟
The cats vs dogs dataset10分钟
Looking at the notebook10分钟
What you'll see next10分钟
What have we seen so far?10分钟
1 个练习
Week 1 Quiz30分钟
完成时间为 7 小时
Augmentation: A technique to avoid overfitting
You've heard the term overfitting a number of times to this point. Overfitting is simply the concept of being over specialized in training -- namely that your model is very good at classifying what it is trained for, but not so good at classifying things that it hasn't seen. In order to generalize your model more effectively, you will of course need a greater breadth of samples to train it on. That's not always possible, but a nice potential shortcut to this is Image Augmentation, where you tweak the training set to potentially increase the diversity of subjects it covers. You'll learn all about that this week!
完成时间为 7 小时
7 个视频 （总计 14 分钟）, 6 个阅读材料, 3 个测验
7 个视频
Introducing augmentation2分钟
Coding augmentation with ImageDataGenerator3分钟
Demonstrating overfitting in cats vs. dogs1分钟
Adding augmentation to cats vs. dogs1分钟
Exploring augmentation with horses vs. humans1分钟
Week 2 Wrap up37
6 个阅读材料
Image Augmentation10分钟
Start Coding...10分钟
Looking at the notebook10分钟
The impact of augmentation on Cats vs. Dogs10分钟
Try it for yourself!10分钟
What have we seen so far?10分钟
1 个练习
Week 2 Quiz30分钟
完成时间为 7 小时
Transfer Learning
Building models for yourself is great, and can be very powerful. But, as you've seen, you can be limited by the data you have on hand. Not everybody has access to massive datasets or the compute power that's needed to train them effectively. Transfer learning can help solve this -- where people with models trained on large datasets train them, so that you can either use them directly, or, you can use the features that they have learned and apply them to your scenario. This is Transfer learning, and you'll look into that this week!
完成时间为 7 小时
7 个视频 （总计 14 分钟）, 5 个阅读材料, 3 个测验
7 个视频
Understanding transfer learning: the concepts2分钟
Coding transfer learning from the inception mode1分钟
Coding your own model with transferred features2分钟
Exploring dropouts1分钟
Exploring Transfer Learning with Inception1分钟
Week 3 Wrap up36
5 个阅读材料
Start coding!10分钟
Adding your DNN10分钟
Using dropouts!10分钟
Applying Transfer Learning to Cats v Dogs10分钟
What have we seen so far?10分钟
1 个练习
Week 3 Quiz30分钟
完成时间为 7 小时
Multiclass Classifications
You've come a long way, Congratulations! One more thing to do before we move off of ConvNets to the next module, and that's to go beyond binary classification. Each of the examples you've done so far involved classifying one thing or another -- horse or human, cat or dog. When moving beyond binary into Categorical classification there are some coding considerations you need to take into account. You'll look at them this week!
完成时间为 7 小时
6 个视频 （总计 12 分钟）, 5 个阅读材料, 3 个测验
6 个视频
Moving from binary to multi-class classification44
Explore multi-class with Rock Paper Scissors dataset2分钟
Train a classifier with Rock Paper Scissors1分钟
Test the Rock Paper Scissors classifier2分钟
A conversation with Andrew Ng1分钟
5 个阅读材料
Introducing the Rock-Paper-Scissors dataset10分钟
Check out the code!10分钟
Try testing the classifier10分钟
What have we seen so far?10分钟
Wrap up10分钟
1 个练习
Week 4 Quiz30分钟
审阅
来自CONVOLUTIONAL NEURAL NETWORKS IN TENSORFLOW的热门评论
由 JM 提供Sep 11, 2019
great introductory stuff, great way to keep in touch with tensorflow's new tools, and the instructor is absolutely phenomenal. love the enthusiasm and the interactions with andrew are a joy to watch.
由 RB 提供Mar 14, 2020
Nice experience taking this course. Precise and to the point introduction of topics and a really nice head start into practical aspects of Computer Vision and using the amazing tensorflow framework..
由 KA 提供Apr 13, 2020
Nice course. Even though I have previously done some projects using CNN and multi-class classification still this course let me to have an insight to how these APIs work. Keep Up The Good Work!!!!!!
由 SS 提供Dec 8, 2019
Very clear explanation on the concepts at the higher level and practical application of it is discussed, demonstrated and also the exercises are of the same way. You will just love learning this way
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.
Looking for more advanced TensorFlow content? Check out the new TensorFlow: Data and Deployment Specialization.
常见问题
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
还有其他问题吗？请访问 学生帮助中心。
© 2020 Coursera Inc.保留所有权利。
