DeepLearning.AI
Convolutional Neural Networks in TensorFlow
DeepLearning.AI

Convolutional Neural Networks in TensorFlow

This course is part of DeepLearning.AI TensorFlow Developer Professional Certificate

Taught in English

Some content may not be translated

Laurence Moroney

Instructor: Laurence Moroney

145,163 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.7

(8,046 reviews)

|

96%

Intermediate level

Recommended experience

16 hours (approximately)
Flexible schedule
Learn at your own pace

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

Shareable certificate

Add to your LinkedIn profile

Assessments

4 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(8,046 reviews)

|

96%

Intermediate level

Recommended experience

16 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

Placeholder

Build your Machine Learning expertise

This course is part of the DeepLearning.AI TensorFlow Developer Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate from DeepLearning.AI
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

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 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!

What's included

8 videos8 readings1 quiz1 programming assignment

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 videos7 readings1 quiz1 programming assignment

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 videos5 readings1 quiz1 programming assignment

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 videos8 readings1 quiz1 programming assignment

Instructor

Instructor ratings
4.8 (1,075 ratings)
Laurence Moroney
DeepLearning.AI
15 Courses486,059 learners

Offered by

DeepLearning.AI

Recommended if you're interested in Machine Learning

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

Showing 3 of 8046

4.7

8,046 reviews

  • 5 stars

    79.18%

  • 4 stars

    15.50%

  • 3 stars

    3.50%

  • 2 stars

    1%

  • 1 star

    0.79%

XN
4

Reviewed on Oct 1, 2019

CM
5

Reviewed on Apr 30, 2019

TM
5

Reviewed on Oct 5, 2020

New to Machine Learning? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions