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

4.7
stars
6,216 ratings
964 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

MS
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!

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.

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901 - 925 of 957 Reviews for Convolutional Neural Networks in TensorFlow

By Alejo G

Oct 6, 2019

A lot of boilerplate code with few new concepts

By Mikołaj M

Oct 12, 2020

The course covers elementary techniques.

By Victor S

Sep 4, 2020

Useful course. Just a bit unstructured.

By Bojiang J

Mar 7, 2020

Content too easy and not engaging....

By Navid H

Sep 15, 2019

I wish it had real assignments

By Samyak J

Aug 2, 2020

exercises are not very clear

By Paula S

Apr 6, 2020

course is a little too easy.

By Pallavi

Mar 12, 2020

It was not great and good

By Yuxuan C

Apr 12, 2020

A little bit too easy.

By Luiz C

Jun 11, 2019

not challenging enough

By Victor M

Mar 19, 2020

Contenido superficial

By Igors K

Oct 26, 2019

I wish it used TF2.

By Masoud V

Aug 21, 2019

Useful but too easy

By Ruxue P

Oct 14, 2020

Too little content

By Gerard C I

Nov 20, 2019

to much shallow

By Rob S

Sep 3, 2020

Good course

By Neshy

Nov 29, 2020

too basic

By Mohammed I A T

Sep 21, 2020

just ok

By Thomas R

Feb 8, 2021

Materials were good for someone who has taken university courses on convolutional networks, but labs were extremely poorly done. Final lab of the course was missing sections for the data generator flow method calls, and augmentation wasn't even tested for. Marker could be improved and provided code can have better sections and maybe an explaining markdown at the top rather than going back and forth. I also noticed that accuracy changed from logs.get('acc') to logs.get('accuracy') which seems to be a tensorflow version issue. I feel overall like the course has been abandoned.

By Li P Z

Jan 19, 2020

If you have taken Andrew's courses in ML or deep learning, you will be disappointed. The amount of content in the videos and exercises is shrunk down by 75% per week. I think a much better job could have been done of structuring the course, and creating meaningful exercises. The instructor does an OK job of showing you how to use TF, but he doesn't always explain things very clearly, and doesn't always have an accurate understanding of how ML or deep learning works.

By 黃文喜

Jun 7, 2020

Content is really useful, but the assignment is really really bad and not user friendly(actually it drives me crazy). For example, instruction is not clear, parameter is outdated(still use 'acc' for accuracy?), assignment cannot be graded not because of modeling. These inconvenience obscure of the importance of learning CNN in TF. For this reason I don't think this course worth more than 3 stars.

By Rishi R

Jul 26, 2020

This course could have covered many more topics in detail, like visualizing individual layers, performing style transfer, saving and loading models, etc. All these were skipped and weeks were wasted on a simple extension of a small concept (image augmentation and multi-class learning) which anyone who glanced at the Keras API could have learnt. I am disappointed at this course frankly.

By Tran N M T

Jul 5, 2020

Really a bad course. Most of the materials can be found online for free on TensorFlow official documentations. Many practices are outdated. Problems with the coding assignment are a nightmare. There is no supervisor to answer many common questions. The code grader checks for very particular things and instructions were not clear at all. In general, this is a pretty bad course.

By Ian P

Feb 18, 2021

The first and fourth graded assignments were not very well posed. The grader in the 4th graded assignment kept running out of memory. The instructors do not get back to people in the forums. There was not much actual new material: most of the 4 weeks of material could have been covered in a single week. This has been the most discouraging coursera course i have taken.

By Ayush M

Dec 8, 2020

Course Material not detailed enough and expected more from it. It does not contain enough variety in exercises and lacks a lot of concepts.

Anyone with good learning (and "overfitting") can complete 1 course in a day.

Final assignment lacked a lot of use case description and it did not even tell us anything about the data or recommended parameters for training.