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

4.7
1,972 ratings
276 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.

PS

Sep 14, 2019

An excellent course by Laurence Moroney on explaining how ConvNets are prepared using Tensorflow. A really good strategy to have the programming exercises on Google Colab to speed up the processing.

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226 - 250 of 276 Reviews for Convolutional Neural Networks in TensorFlow

By H M A r

Oct 02, 2019

The course is really nice. But would be better if the convolutional layers were a bit more detailed. It was a bit difficult for me to understand all the parameters e.g: input/output filter size.

By KHODJA

Oct 02, 2019

A more advanced course would be highly appreciated.

By Vittorio R

Oct 06, 2019

Good, but expected more, for example object detection.

By Brian ( B

Oct 23, 2019

very practical courses on implementation of CNN in tensorflow. Suggest student also take the deeplearning series with this series.

By Leon R

Oct 26, 2019

Loved the course. I would have liked a module on saving your own models and then loading them later. The Inception one is nice, but it comes with some "niceties" that I don't think you have with loading a home grown model.

By Damon W

Oct 08, 2019

Good practical course. A bit heavy on visual images, but very informative.

By Lei W

Oct 30, 2019

will be nice to have non-third party programming exercises that are graded by Coursera

By Thomas L

Nov 04, 2019

Maybe a bit repetitive, when you just finished Course 1. We see a lot of lines of codes explained again from course 1 and I think that could be avoided.

However, the new concepts are nicely introduced and very interesting to implement!

By Walter G

Nov 29, 2019

A very brief quick course.

By Venkatesh S

Dec 02, 2019

Excellent!

By Bingcheng L

Nov 12, 2019

quite easy

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 Phuoc H L

Nov 14, 2019

More exercises should be available for students to practice and test their skills.

By Vitalii S

Nov 25, 2019

Too easy with good background and fast passing course.

By Sebastian M A

Dec 06, 2019

No le pongo las 5 estrellas por la falta de ejercicios prácticos calificables.

By Muhammad U

Dec 07, 2019

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By Luiz C

Jun 11, 2019

not challenging enough

By Renjith B

Jul 15, 2019

Good content for classification tasks. But didn't cover anything related to object recognition, localisation and semantic segmentation which are the challenging computer vision tasks.

By Michael

Jul 26, 2019

A bit too basic and shallow in terms of conducting the lecture. You are left doing most of the things on your own as the trainer assumes you know. Like using the jupyter notebook, configuring the tensorfow. Some of the google collab books do not work or took too long to load, the videos are too short no notes provided at all. After finishing the course there is nothing to refer to and its starting all over again. Given the level of machine learning course with Professor Adrew Ng, the standard is very high and you will expect that same level. Nevertheless, the concepts are very useful and the lecture explain very well. There level of material left for students to practice on their own,like assignments, notes. Not to be referred to existing material.

By Joey Y

Aug 04, 2019

The course seems to be getting more loose than the first course.

By Muthukumarasamy S

Aug 04, 2019

Overall learning from this course is less compared to the expectations from a 4 week course. I was expecting to learn variety of TensorFlow implementations for CNN like Face recognition, Object detection. But this course only talks about Image classification. It would have been better if you could also discuss more about implementing various architectures in TensorFlow like ResNets, Inception. Also, You talked only about using sequential layers in Keras and concatenation of layers in Keras is not discussed here. I know all these concepts are discussed in Deep Learning specialization. I was only expecting to learn their implementation in TensorFlow from this course.

By Wellington B

Aug 05, 2019

need to watch Andrew Ng's course on deep learning before watching this one

By Ethan V

Aug 17, 2019

Solid content, but it feels like it's not *very* much on top of the first course in this specialization. I think these two courses could be combined into one.

By Masoud V

Aug 21, 2019

Useful but too easy

By Shubham A G

Aug 25, 2019

Lacks depth and complexity. The course is geared more towards complete newbies or high school graduates.