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.
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TensorFlow in Practice 專項課程deeplearning.ai課程信息
1,295,290 次近期查看
學生職業成果
40%
完成這些課程後已開始新的職業生涯
43%
通過此課程獲得實實在在的工作福利
12%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 1 門課程（共 4 門）
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30 小時
英語（English）
字幕：英語（English）, 西班牙語（Spanish）, 俄語（Russian）, 日語
您將學到的內容有
Learn best practices for using TensorFlow, a popular open-source machine learning framework
Build a basic neural network in TensorFlow
Train a neural network for a computer vision application
Understand how to use convolutions to improve your neural network
您將獲得的技能
Computer VisionTensorflowMachine Learning
學生職業成果
40%
完成這些課程後已開始新的職業生涯
43%
通過此課程獲得實實在在的工作福利
12%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 1 門課程（共 4 門）
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30 小時
英語（English）
字幕：英語（English）, 西班牙語（Spanish）, 俄語（Russian）, 日語
提供方
deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 6 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.
完成時間為 6 小時
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
Before you begin: TensorFlow 2.0 and this course10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
完成時間為 7 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!
完成時間為 7 小時
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 vision1小時
See how to implement Callbacks10分鐘
Week 2 Resources10分鐘
1 個練習
Week 2 Quiz
完成時間為 8 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.
完成時間為 8 小時
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 yourself1小時
Experiment with filters and pools1小時
Week 3 Resources10分鐘
1 個練習
Week 3 Quiz
完成時間為 9 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!
完成時間為 9 小時
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
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 classifier1小時
Get hands-on and use validation30分鐘
Get Hands-on with compacted images30分鐘
Week 4 Resources10分鐘
Wrap up10分鐘
1 個練習
Week 4 Quiz
關於 TensorFlow in Practice 專項課程
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.
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