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 DeepLearning.AI TensorFlow Developer Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
Convolutional Neural Networks in TensorFlow
This course is part of DeepLearning.AI TensorFlow Developer Professional Certificate
Instructor: Laurence Moroney
Sponsored by Coursera for Reliance Family
154,924 already enrolled
(8,158 reviews)
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What you'll learn
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
Skills you'll gain
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There are 4 modules in this course
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, and you learned a little bit about Convolutional Neural Networks (ConvNets). In this course you'll go deeper into using ConvNets with 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!
What's included
8 videos8 readings1 assignment1 programming assignment1 ungraded lab
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!
What's included
7 videos4 readings1 assignment1 programming assignment2 ungraded labs
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!
What's included
7 videos4 readings1 assignment1 programming assignment1 ungraded lab
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!
What's included
6 videos7 readings1 assignment1 programming assignment1 ungraded lab
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Reviewed on Oct 1, 2019
The course is really nice. But would be better if the convolutional layers were a bit more detailed. It was a bit difficult for me to understand all the parameters e.g: input/output filter size.
Reviewed on Sep 29, 2019
The course was fine sometimes I feel too easy. I would like to see more of the available options for the layers, such as padding, stride. filter size, mean average, batch normalization, etc...
Reviewed on Oct 5, 2020
Excellent and detailed on how to create a convolutional neural network using TensorFlow as well as explaining how to solve problems such as low accuracy, overfitting and even improving the dataset.
Recommended if you're interested in Data Science
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