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

4.7
stars
6,038 ratings
919 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 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.

RB
Mar 14, 2020

Nice experience taking this course. Precise and to the point introduction of topics and a really nice head start into practical aspects of Computer Vision and using the amazing tensorflow framework..

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826 - 850 of 913 Reviews for Convolutional Neural Networks in TensorFlow

By Ted T

Jan 2, 2021

Lawrence's lectures were good, but exercises were disconnected from course material. Having to do exercises in Google Colab and then redo in Jupyter notebook was inefficient.

By Andrea B

Jun 1, 2020

the topic is interesting, and the course is quite hands-on, but the treatment of the subject is extremely basic. Videos are too short and somehow superficial and incomplete

By Seif M M

Sep 20, 2020

very good course, but think it needs to go deeper in the functions and tools in tensorflow for conv netwroks, i have the feeling that the course is somehow shallow.

By Adnan P

Jun 8, 2020

It was a great course, but in my opinion, it could have been even better if it involved more concepts & APIs to explore apart from the most in-use TensorFlow APIs.

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 Madhav A

Oct 16, 2019

The course is good for beginners as it is very basic. It needs more advance topics like Detection using TensorFlow. Have a lot of scope for improvement.

By Alejandro B G

Sep 3, 2019

Google colab system for tasks is pretty bad, no control on the tasks plus it erases and u can't prove you did the work unless you save it

By ABHAS B

Apr 9, 2020

The course content is excellent. The talks with Andrew are inspiring, but the assignment graders are aweful and a big turn off.

By Ameya D

Jun 17, 2020

This course is more of hands on activity in tensorflow. You need to have good understanding of CNN prior to doing this course.

By Amit C

Mar 18, 2020

Content is very limited.I wish they could have gone in-depth covered more areas of CNN like object detection ,segmentation etc

By Jingwei L

Aug 30, 2019

The course is taught excellently. However, there are overfull file stream operations in Python that the course does not cover.

By Marc-Antoine G

Nov 13, 2019

Please make the "Ungraded assignment" Graded and add more comments/directive in them to make sure we understand each steps.

By Samuel K

Nov 2, 2019

Clear explanations. Good sample codes. Too easy. Doesn't go deep enough in terms of theory. Exercises should be mandatory.

By Daniel D

Mar 26, 2020

Pros: the course teaches CNNs clearly and concisely.

Cons: the memory issues on the last assignment wasted a lot of time.

By David H

Nov 16, 2019

Not solid enough and the exercise could be more organised. For example: some of the data downloading links didn't work.

By Shreenivas

Dec 22, 2020

Good content. The coding and assignments need significant improvement. There is no support whatsoever in assignments.

By Sailesh G

Nov 2, 2019

Expected a lot more in this course from the Tensorflow specialization. Something that'd take us beyond tf.keras.

By Mohammed F

Jul 6, 2019

Could have dived more into the details and inner workings of Convolutional layers but overall awesome course.

By Aleksey V

Nov 22, 2019

complex ideas in very basic tasks that you can easily accomplish by copy-pasting from provided notebooks.

By Shubham A G

Aug 25, 2019

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

By Frank W

Jul 17, 2020

The programming tasks are not very helpful. The main difficulty is that unknown methods should be used.

By Michael E

Feb 14, 2020

Would like to have seen some information on techniques such as batch normalization and residual layers.

By Nahum P

Sep 22, 2020

Final work had almost no connection to what you learned during the course.

Not enough hands on.

By Geoff G

Aug 17, 2020

The topics are explained very briefly.There is no in depth coverage of the mentioned topics

By Sajal C

Mar 31, 2020

Course is good however there can be programming assignments for better practice.