Chevron Left
Back to Convolutional Neural Networks in TensorFlow

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

4.7
stars
8,212 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 DeepLearning.AI TensorFlow Developer 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 Developer 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....

Top reviews

AK

Jun 4, 2020

Laurence Moroney is the best. Before taking up the course, i didnt know anything about the AI or ML or Tensorflow. The concepts were explained in such a manner that anyone can learn Tensorflow.

TM

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.

Filter by:

1026 - 1050 of 1,269 Reviews for Convolutional Neural Networks in TensorFlow

By Paul Z

•

Dec 19, 2020

Very helpful, however, the last exercise was misguided.

By Salem S

•

Apr 2, 2020

apart from the technical issue, the course is fantastic

By Vital

•

Nov 25, 2019

Too easy with good background and fast passing course.

By Vittorio R

•

Oct 6, 2019

Good, but expected more, for example object detection.

By Taras B

•

Mar 23, 2022

It will be very nice to have more coding exercices.

By Haoran C

•

Sep 4, 2019

Please transfer the notebook from CoLab to Coursera.

By Ashish R

•

Aug 23, 2024

Four stars because ImageDataGenerator is outdated .

By Robert G

•

Dec 11, 2019

I would like to see examples with videos, yolo, etc

By KHODJA

•

Oct 2, 2019

A more advanced course would be highly appreciated.

By Ruiwen W

•

Jul 22, 2020

Assignment material not very aligned with lectures

By Md. S U I

•

Jan 24, 2024

Very Basic. Should Have more detailed information

By Gerardo S

•

Sep 16, 2020

I feel like this series of courses is too narrow

By Ahmet K

•

Dec 30, 2019

Nice course! All detailed and explained. Thanks!

By Jay T

•

Sep 1, 2020

A bit hard to understand the final assignment.

By Kailyn W

•

Sep 9, 2019

I need more coding practice, not just quizzes.

By Michel M

•

Aug 6, 2019

The final assignment was somewhat a steep step

By Zhi Z

•

Jul 6, 2019

A good course for Keras but not for tensoflow.

By Aleksander W

•

Feb 14, 2021

better than course #1 of this specialisation

By Surya n T S

•

Oct 24, 2021

A very practical approach towards learning.

By Prabhat K G

•

May 29, 2020

Last assignment needs much more explanation

By RAVI P

•

Jun 17, 2020

Programming exercises are a bit confusing.

By Dr. S G

•

Apr 17, 2020

Learned many things about computer vision.

By Zanuar E R

•

Jan 18, 2022

Good Course, I have learned a lot from it

By zhizhen w

•

Aug 10, 2020

a bit too easy for professional engineer

By Yu-Chen L

•

Jun 18, 2020

Maybe could be better with more content.