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Learner Reviews & Feedback for Convolutional Neural Networks in TensorFlow by DeepLearning.AI

7,683 ratings

About the Course

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 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 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....

Top reviews


Nov 12, 2020

A really good course that builds up the knowledge over the concepts covered in Course 1. All the ideas are applicable in real world scenario and this is what makes the course that much more valuable!


Sep 11, 2019

great introductory stuff, great way to keep in touch with tensorflow's new tools, and the instructor is absolutely phenomenal. love the enthusiasm and the interactions with andrew are a joy to watch.

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851 - 875 of 1,184 Reviews for Convolutional Neural Networks in TensorFlow

By Rajesh R

Jun 14, 2020

Great course to learn newer aspects of TF. For me a great revision of ConvNets and a confidence builder. If there's one thing I'd fix, it would be the autograder and how often it crashes.

By Amit G

Jun 24, 2021

I liked this course, and also the way progression is taking place but the time required to complete this course needs to be reevaluated. This course can be finished in 8-10 hours easily.

By Dr. A K D

Sep 24, 2020

Found the hands-on not very interesting. Couple of them focussed on file handling and stuff rather than on more important stuff that getting into the hoods of transfer learning, etc.

By Rakesh G

Jan 16, 2020

I think this was a good course but the standard of exercises and quizzes was too easy. More conceptual questions especially in quizzes would help in understanding the topic better

By ashish s

Apr 22, 2020

Overall good. Could have gone in bit more depth on how various hyper parameter tuning and regularization methods impact the model training. Provide some best practices tips .

By Gerardo S

Sep 26, 2019

the last exercise needed a big upload, made it imposible (for me) to do. This was a problem not related to the subject, should use data downloadable directly from internet.

By Sokratis A

Mar 30, 2021

Most of the Programming Assignments are a copy-paste routine from colab notebooks provided

the last one was pretty challenging though ^_^

(Im experienced in Programming)

By Eric L

Dec 10, 2020

This course only requires few hours of work and I would like to see more depth. The parts on image augmentation and transfer learning were pretty interesting though!

By Luciano C

Dec 31, 2020

Curso muito legal, a única coisa que ficou um pouco abaixo do esperado foi o último exercício da quarta semana, não foi construído com o cuidado visto nos outros.

By Vishwanadha K V

Jun 22, 2020

The assignments are not challenging enough. The concepts are really well explained and for someone with no background in this area, this is a great learning asset

By Dimitry I

Aug 10, 2019

Very good course that teaches you basics of convolutions, augmentation, transfer learning. Thank you to Mr. Moroney and the Coursera team for making it available.

By vaibhav t

Jun 26, 2020

The course was good. The only problem was the last assignment where some of the functions went missing. It was difficult for a beginner to catch such glitch

By chaitanya m

Apr 15, 2020

The best course to do. Especially after the specialization course from Andrew. It is really helpful to code all the concepts you learned from Andrew course.

By Ujjwal G

Nov 16, 2019

I think most much of the course conent was same as the first course, this course could have been a little more advanced. But overall a great place to start.

By Moritz R

Jan 24, 2021

Very nice the step from the first course was really nice. The achievements were harder to reach an all over the cose was less buggy than the first one. :)

By Shubham G

Jan 29, 2022

Last coding assignment was not clear. Eg- Why is there a model.evaluate line at the end when we are already checking validation accuracy in fit generator

By Estefania T

Jun 2, 2020

The contents are a bit light from my point of view. I get it is to be accessible for more people but math and explanations are in some cases important

By Toqa A M

Apr 4, 2021

it was great course but I need some more details and the speaking was a little difficult as some of words are slang and the translation was so bad

By Subham S

Dec 23, 2019

The course content was quite good and overall understandable but the exercises and quizzes were quite easy, they could have been more challenging

By Pray S

Apr 23, 2020

The hand sign assignment need more explanation about using flow object from ImageDataGenerator since I just know only flow_from_directory object


Feb 14, 2021

Learn a lot for CNN in this course, but require advance knowledge in Python & Numpy to understand the code as it was not explain in the course.

By 屈佑平

Nov 28, 2020

Exercise_4_Multi_class_classifier_Question-FINAL has problem if you entirely follow the tips, you can find the correct code in the forums.

By Gianluca T

Sep 17, 2020

Very nice and interesting videos, cool concepts, amazing datasets. Exercises lack sometimes clear objectives, or provide unclear feedbacks

By Rodolfo V

Jul 10, 2020

I guess one thing was not studied, the method .flow() which get the images generated by keras with the dataset labeled for the final test.

By Caroline B

Jul 14, 2022

The tutorials were easy to follow and I think the assignments were nice because some parts were easy, but some parts had some challenges.