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

4.7
stars
6,981 ratings
1,087 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!

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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951 - 975 of 1,087 Reviews for Convolutional Neural Networks in TensorFlow

By Salih K

Nov 9, 2020

The course itself is really good; however, homework problems at the end of the chapters are very unorganized. There is almost no guide at all. You may end up spending hours while trying to figure out why grader is having problems or your model's accuracy is very low.

By Varun C

Jul 10, 2020

Giving it 3 stars because of the last week's assignment. There is little to no information about the dataset and the learner is just expected to know how to deal with the data. No information on how many classes to expect as output and other necessary information.

By Ambroise L

Dec 29, 2019

What could improve it: Not enough depth in the practicals if you have already done Andrew Ng's course on Conv nets. No graded practical exercise.

What was good: Clear examples, Good setup to experiment with the algorithms & Speak explains concepts very clearly,

By Ignacio R L

Mar 28, 2020

Good course, but the notebooks need a deep review to fix the problems related to balance between the requirements of the exercise and the resources available also a better explanation of the exercise aims would be a nice to have to avoid misunderstandings

By Michael R

Sep 18, 2019

Actually a great course. Only not getting more stars due to the issue encountered with the last exercise where there is an issue in loading the data files. The workbook keeps on crashing and there is no solution provided to resolve that.

By Matías B

May 28, 2020

The material is good, but there is not much thereof.

The duration of the assignmentsis greatly exaggerated, since most of the lengths for the readings and exercises are wrong.

The course can easily be done in 25% of the official time.

By Dirk H

Nov 7, 2019

If you have taken the first course of the specialization this class was repetitive at some points. I also did not like that there have not been graded coding problems. I still got some practice and learned some new techniques.

By Wenyu Y

Mar 20, 2020

The materials about implmentation of transfer learning is helpfu, but again, I think the whole content of the first two courses could be compressed into one week. There're really not too much new things.

By Sumit c

May 18, 2020

some clear instructions should be given for students. In exercise of week 4, there was no specific instruction about using .flow instead of .flow_from_directory, for labels we had to use to_catagorical.

By Amir S

May 24, 2020

Course assignments need a good overhaul. The two environments to practice the assignments (Jupyter workbooks and Google Colab) are not consistent, one throws an error while the other one works fine.

By Nermeen M M

Dec 13, 2019

Very good course but please consider reordering the videos and reading especially in week 3. It is better to discuss the code in the video before moving to the notebook not the opposite.

Thank you

By Ashok N

Jun 26, 2020

Course content was super nice.

But exercise organization is very annoying. not at all satisfied with the exercises. sometimes not loading and sometimes is really annoying . very disappointed

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 Luis S

Feb 9, 2021

The essential of convolutional neural networks is covered by this course although there ais unnecessary code in the examples and a lack of explanations especially in the assignments.

By Yuvraj G

Apr 11, 2020

Too basic course. If its a practical course, then there should be exposure to more functionality of keras and not just the basic one which can be done from a blog/documentation.

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 Andrei I

Feb 13, 2021

Too easy. One can finish all exercises without learning much. The quality of explanations is poor. The whole course is but a short walk through Laurence's Jupiter notebooks.

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 Michele M C

Feb 18, 2021

cnn implementation theory should be covered better, giving more reason why the code is written this way, furthermore the last homework of the course was bad designed

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

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 Moeen T

Feb 19, 2021

There wasn't enough useful content. There were also many problems with the programming assignments, specially in the last week's assignment.

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