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.
In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models.
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 enroll in the first two courses today. We recommend starting with Course 1: Introduction to TensorFlow for AI, ML, and DL.
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deeplearning.ai
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课程信息
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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
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 4 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!
In Week 1, this week, you'll get started by looking at a much larger dataset than you've been using thus far: The Cats and Dogs dataset which had been a Kaggle Challenge in image classification!
8 个视频 （总计 18 分钟）, 6 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 Outro33
6 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
Getting ready for the exercise10分钟
1 个练习
Week 1 Quiz30分钟
周
2完成时间为 4 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!
7 个视频 （总计 14 分钟）, 7 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 Outro37
7 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
Getting ready for the exercise10分钟
1 个练习
Week 2 Quiz30分钟
周
3完成时间为 4 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!
7 个视频 （总计 14 分钟）, 6 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 Outro36
6 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
Getting ready for the exercise10分钟
1 个练习
Week 3 Quiz30分钟
周
4完成时间为 4 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!
6 个视频 （总计 12 分钟）, 6 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
Outro, A conversation with Andrew Ng1分钟
6 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
Getting ready for the exercise10分钟
Outro10分钟
1 个练习
Week 4 Quiz30分钟
4.8
23 个审阅热门审阅
A very comprehensive and easy to learn course on Tensor Flow. I am really impressed by the Instructor ability to teach difficult concept with ease. I will look forward another course of this series.
A patient and coherent introduction. At the end, you have good working code you can use elsewhere. Remarkably, the primary lecturer, Laurence Moroney, responds fairly quickly to posts in the forum.
讲师
Laurence Moroney
AI AdvocateGoogle 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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