DeepLearning.AI
Convolutional Neural Networks in TensorFlow
DeepLearning.AI

Convolutional Neural Networks in TensorFlow

Laurence Moroney

Instructor: Laurence Moroney

153,741 already enrolled

Gain insight into a topic and learn the fundamentals.
4.7

(8,134 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 16 hours
Learn at your own pace
96%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.7

(8,134 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 16 hours
Learn at your own pace
96%
Most learners liked this course

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

Details to know

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Assessments

4 assignments

Taught in English

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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

Instructor

Instructor ratings
4.8 (1,095 ratings)
Laurence Moroney
DeepLearning.AI
19 Courses522,632 learners

Offered by

DeepLearning.AI

Recommended if you're interested in Machine Learning

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