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 Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
The full deeplearning.ai TensorFlow Specialization will be available later this year, but you can get started with Course 1, Introduction to Tensorflow for AI, ML and DL, available now on Coursera.
deeplearning.ai
課程信息
4.7
266 個評分
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
教學大綱 - 您將從這門課程中學到什麼
週
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
週
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
週
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
週
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
4.7
51 個審閱熱門審閱
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?
A very nice Introductory course, and a very well explained lectures. The teaching is very interactive and you can complete the course in a short and equipped with many skills.
講師
Laurence Moroney
AI AdvocateGoogle Brain
關於 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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