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Learner Reviews & Feedback for Convolutional Neural Networks in TensorFlow by deeplearning.ai

4.7
stars
2,444 ratings
355 reviews

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 deeplearning.ai 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 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

JM

Sep 12, 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.

MH

May 24, 2019

A very comprehensive and easy to learn course on Tensor Flow. I am really impressed by the Instructor ability to teach difficult concept with ease. I will look forward another course of this series.

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226 - 250 of 355 Reviews for Convolutional Neural Networks in TensorFlow

By Chirag S

Feb 11, 2020

Good course!

By Volodymyr P

Nov 19, 2019

Great course

By 李英斌

Sep 15, 2019

nice course!

By neko@live.it

May 27, 2019

FUN AND EASY

By Mohammad Z

Jan 19, 2020

Exceptional

By Vishal S

Jan 12, 2020

Nice course

By Gourav B

Aug 06, 2019

good course

By rudraps

Sep 05, 2019

Thank you.

By Shahzeb I

Nov 21, 2019

excellent

By M n n

Oct 22, 2019

Good One!

By Joe J

Sep 23, 2019

excellent

By ANURAG A

Aug 30, 2019

excellent

By Hasib Z

Aug 17, 2019

The best!

By Gaurav R P

Aug 08, 2019

Too basic

By hitashu k

Jan 13, 2020

AWESOME

By Kavya B

Dec 05, 2019

awesome

By zhenzhen w

Nov 18, 2019

nice

By Jurassic

Sep 06, 2019

good

By 林韋銘

Aug 20, 2019

gj

By Nicolas

Aug 30, 2019

First, I think the course was great, very instructive. Thanks to Andrew and Laurence for putting this together, is a great source of information to understand more about DL. Some things I think could improve the course.

I found the transfer learning lessons a bit unclear and I struggle generalizing this to other cases. Also, I was a bit confused by the flow of the course. The course starts with a multi classifier (or actually, the previous course), then the lessons focus on binary classifiers and it ends again with multi classifiers, because these should be the more complex ones.

One last technical thing, only on the last lesson of this course it is mentioned that the classifiers output the probabilities on alphabetical order when using ImageDataGenerators (or at least, that's my impresision). I've wondered since the course introduced the ImageDataGenerators, how the probabilities are assigned on the outputs. I could figure out on the sigmoid that the classifier would look for the first class on the directory and output 1 or 0 based on that, but it would be good to have this mentioned at some point on the video when the ImageDataGen is introduced.

Thanks again! Great course

By João A J d S

Aug 03, 2019

I think I might say this for every course of this specialisation:

Great content all around!

It has some great colab examples explaining how to put these models into action on TensorFlow, which I'm know I'm going to revisit time and again.

There's only one thing that I think it might not be quite so good: the evaluation of the course. There isn't one, apart from the quizes. A bit more evaluation steps, as per in Andrew's Deep Learning Specialisation, would require more commitment from students.

By Anand H

Sep 12, 2019

One challenge i have faced is with deploying the trained models. I find very little coverage on that across courses. It's one thing to save a model.h5 or model.pb. It would be nice if you can add a small piece on deployment of these models using TF Serving or something similar. There is some distance between just getting these files outputted and deploying. TF documentation is confusing about some of these things. Would be nice if you can include a module on that.

By Oleksiy S

May 23, 2019

Exellent tutorial for using Tensorflow and convolutional networks. Useful usage examples, interesting and challenging exercises. A few minor mistakes prevent five star grading. But please note that mistakes happen and we have to live with this :-). Nice work, looking forward for the next course of the specialization.

By Henk M

Dec 22, 2019

This course explores the topics of the first course for image classification with neural networks. All the tests are multiple choice questions. There are some code examples to work with as well as extra exercises but it would have been good to have a programming test as well.

By Saeif

Aug 20, 2019

This is another great course in the specialization. I wish only there were graded exercises like the previous course that we can submit and get a grade for. I understand maybe this is due to the long time of training and that is not possible to do.