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.
Convolutional Neural Networks in TensorFlow
proposé par
deeplearning.ai
10,961 inscrit(s)
À propos de ce cours
243,404 consultations récentes
Ce que vous allez apprendre
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
Compétences que vous acquerrez
Inductive TransferAugmentationDropoutsMachine LearningTensorflow
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
14 heures pour terminer
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 vidéos (Total 18 min), 6 lectures, 3 quiz
8 vidéos
A conversation with Andrew Ng1 min
Training with the cats vs. dogs dataset2 min
Working through the notebook4 min
Fixing through cropping49s
Visualizing the effect of the convolutions1 min
Looking at accuracy and loss1 min
Week 1 Outro33s
6 lectures
Before you Begin: TensorFlow 2.0 and this Course10 min
The cats vs dogs dataset10 min
Looking at the notebook10 min
What you'll see next10 min
What have we seen so far?10 min
Getting ready for the exercise10 min
1 exercices pour s'entraîner
Week 1 Quiz30 min
Semaine
24 heures pour terminer
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 vidéos (Total 14 min), 7 lectures, 3 quiz
7 vidéos
Introducing augmentation2 min
Coding augmentation with ImageDataGenerator3 min
Demonstrating overfitting in cats vs. dogs1 min
Adding augmentation to cats vs. dogs1 min
Exploring augmentation with horses vs. humans1 min
Week 2 Outro37s
7 lectures
Image Augmentation10 min
Start Coding...10 min
Looking at the notebook10 min
The impact of augmentation on Cats vs. Dogs10 min
Try it for yourself!10 min
What have we seen so far?10 min
Getting ready for the exercise10 min
1 exercices pour s'entraîner
Week 2 Quiz30 min
Semaine
34 heures pour terminer
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 vidéos (Total 14 min), 6 lectures, 3 quiz
7 vidéos
Understanding transfer learning: the concepts2 min
Coding transfer learning from the inception mode1 min
Coding your own model with transferred features2 min
Exploring dropouts1 min
Exploring Transfer Learning with Inception1 min
Week 3 Outro36s
6 lectures
Start coding!10 min
Adding your DNN10 min
Using dropouts!10 min
Applying Transfer Learning to Cats v Dogs10 min
What have we seen so far?10 min
Getting ready for the exercise10 min
1 exercices pour s'entraîner
Week 3 Quiz30 min
Semaine
44 heures pour terminer
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 vidéos (Total 12 min), 6 lectures, 3 quiz
6 vidéos
Moving from binary to multi-class classification44s
Explore multi-class with Rock Paper Scissors dataset2 min
Train a classifier with Rock Paper Scissors1 min
Test the Rock Paper Scissors classifier2 min
Outro, A conversation with Andrew Ng1 min
6 lectures
Introducing the Rock-Paper-Scissors dataset10 min
Check out the code!10 min
Try testing the classifier10 min
What have we seen so far?10 min
Getting ready for the exercise10 min
Outro10 min
1 exercices pour s'entraîner
Week 4 Quiz30 min
4.8
19 avisMeilleurs avis
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.
Excellent material superbly presented by world-class experts.\n\nSorry if this sounds sycophantic, but this series contains some of the best courses I've encountered in50+ years of learning.
Enseignants
Laurence Moroney
AI AdvocateGoogle Brain
À propos de 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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