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

4.7
stars
2,324 ratings
334 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.

SS

Dec 09, 2019

Very clear explanation on the concepts at the higher level and practical application of it is discussed, demonstrated and also the exercises are of the same way. You will just love learning this way

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276 - 300 of 332 Reviews for Convolutional Neural Networks in TensorFlow

By rajesh t

Jan 12, 2020

Need more depth and real life scenarios.

By Muthiah A

Jan 06, 2020

Useful continuation for practitioner.

By Renato R

Jan 05, 2020

needs to be more advanced too basic

By Muhammad U

Dec 07, 2019

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By Yufei M

Jul 26, 2019

I think the quiz should be harder

By Xinhui H

Sep 16, 2019

Some overlap with first course.

By Cheng H Z

Dec 18, 2019

Too little things were covered

By Walter G

Nov 29, 2019

A very brief quick course.

By Guilherme R M

Jun 10, 2019

Bom curso, muito prático.

By Prabesh G

May 23, 2019

Okey.. So easy but okey

By Patrick L

Dec 26, 2019

I like this course

By Vivek S

Jun 24, 2019

Super cool stuff!

By ashraf s t m

Jul 31, 2019

Voice is low

By Venkatesh S

Dec 02, 2019

Excellent!

By Bingcheng L

Nov 12, 2019

quite easy

By hamzeh a

Aug 06, 2019

Very Cool

By Omar M

Jul 16, 2019

Was okay

By Henrique C G

Jan 02, 2020

I'm sad to say that I'm really disappointed with the course. What is even stranger is that professor Andrew is associated and endorse the course. I like professor Marooney, but honestly, even his free tutorials on the Tensorflow channel on Youtube have more information than this course. It really seems like something put together in a haste just to make it available on Coursera. The level of detail and instructions is not on par with the quality of both the Coursera platform and the professors associated with this course.

It seems that as I progress through the courses in this specialization the instructions get poorer and poorer and the level of information gets more and more scarce. It got to a point where we are just given notebooks to run; they are not even graded (they barely were on the first course). And even the notebooks where the we are given a chance to complete some code, there are absurd things like "print(#your code here#)" in places that don't even make sense except if we copy and paste from the other notebooks of the course. Really? Print what? The only way we can guess what kind of debug info the notebook is asking is by looking at other notebooks at that exact same line.

For the reviewers; if you are really reading this, please remember that Coursera is charging $49/month for this specialization. If we consider that an average student will take 4 weeks to complete, that's almost $200 for something that's barely a tutorial at it's current version. $49 may be a reasonable rate for a citizen of the US, for example, but it's and exorbitant amount of money for students of poorer countries using the platform in hopes of acquire knowledge of decent quality.

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 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 Bakhtawar U R

Dec 09, 2019

Good but too basic.

Specialization's first course already covered the basic of tensorlfow. This course is suppose to expose to sota topics in computer vision using cnns. The content in this course can be easily fetched from many online forums. Thus the curators need to put some advance topic like attention, spatial transformer etc etc

By Philip D

Sep 05, 2019

A good course, but again, not nearly as in depth as the original deeplearning.ai set of classes. The material feels introductory and at times superficial, with no real work required of the student to complete the class. At best a very early start to using convolutional networks with the keras apis in tensorflow.

By Raul D M

Nov 01, 2019

It is a good course for a fast overview on this topic. Be aware that it is not an introduction on ConvNN (but there are several courses of deeplearning.ai on this topic). If you are looking for a detailed course on Tf for ConvNN, I suggest you a book, the official documentation.

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 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.