This is really a great course. The professor teaches the use of Tensorflow from very simple models to fairly real-life examples. He also raises inspirational questions and leads us to playing around the codes to better understand the theory behind outputs.

Really well paced course and amazing material. Exercises leave you wanting more, in the sense that they really spark your interest to research more about the topic. Assignments are well structured and lend themselves to a seamless learning process. Thanks!

It is good introductory course for tensor flow and CNN. I had previously been working using tensor flow at my job and a GATECH OMSCS Graduate so this one wasn't too bad but help to fill in some gaps and showed me some helpful tips when using tensorflow!

This is wonderful course. I always wanted to learn Deep Learning concept and apply on real image data, but I did not know where to start. This course gives me a path to learn and apply deep learning on images using TensorFlow with very few lines of code.

Really helpful course, everything that they teach becomes really easy to understand. This course has elevated my interest in AI and I am really thankful to courser for providing such a course for students and spreading the knowledge throughout the globe.

A good course to take after finishing up the deep learning specialization from deeplearning.ai. The videos are great review, and the assignments have much less completed for you meaning you have to do the work and learn out to practically use TensorFlow!

Solid course. The exercises are a bit too much pre-setup, but not by much. Layer visualization is used extensively but unfortunately not covered at all. I think it should be as it is important for understanding and troubleshooting. Nevertheless, 5 stars.

Good introduction to Tensorflow and its semantics. Looking forward for another courses in this lineup.

Thank you Moroney, Andrew Ng and Coursera for a fantastic introduction course.

I loved the projects at the intro stage, and these were so well explained.

I think this is the course that i need which provide me explanation of each line of code and not assume that we know that concept previously and also with hands on practice on google colab i am more confident on skill that i gained through this course .

A basic introductory course, good introduction to get up and running with basic Tensorflow. Did a good job focusing on the task at hand and a good job referencing outside material for depth where it was useful but out of scope for the aims of the class.

This course helped me to learn Deep Learning in the same way a machine learns from data. Without having much theoretical knowledge, now I can build basic model & run it. Thanks a lot to deeplearning.ai team for proving such a practical oriented course.

I really like the way the course was simplified. I would recommend people to explore Machine Learning and Deep learning specialization offered by Andrew NG for in-depth knowledge of the concepts. Overall, this course was amazing and my time worth spent.

Tried multiple MOOCs to learn tensorflow. By far the simplest to catch up on and most well explained. Explanations were given were each line of codes, quite literally. The practice notebooks helped out a lot in getting my own hands dirty with the code.

Its really superb course. Easy to understand the image classification problem and solve using deep learning neural networks. Practical approach and hands on assignment helps to get confidence for using CNN and DNN for image classification applications.

for what it was that it is great. there were some hacky learning curves, i.e., all of the problems can be solved using creative copy and pasting of previous material. But I thought, hey, if I know the code well enough that I can do that I am learning.

Labs are awesome! you learn by doing! I really appreciate the explanation of each parameter in tensorflow, which helps me understand how to use it in different scenarios. I will recommend this course to anyone that is interested in machine learning.

Super cool! Nice introduction to Tensor Flow and Machine Learning in a good pace and with good examples! Out of this introduction, I was already able to create my own spaghetti identifier (using the same techniques as from horse vs humans example!)

It was an amazing and intuitive course, the videos were short, intuitive and to the point so you would not loose concentration. I would like to give a suggestion about the lab, please include details about the dataset in the lab notebooks as well.

This course not only has great contents also have a straight forward way to teach introduction for Deep Learning with TensorFlow. They skipped the complex math and Neural Network components in the front to focus on Machine Learning cases to solve.

Great Course!!! I am very grateful to deeplearning.ai team from providing a platform to learn this kind of stuff which not only provides new employment opportunities but also provide confidence to do a lot interesting stuff !!!

Thank you very much!

The course is really nicely designed. The approach is really practical and it takes you directly into how to work with the TensorFlow library. I would highly recommend you taking this course if you want to quickly learn the tensorflow library.

Excellent, you learn the tools in code to go straightforward from theory to coding, solving the problem of a lot of code read large sets of images and label their classification. The course also reinforce fundamental concepts of machine learning.

Great course - especially considering how easy Keras makes in creating DNNs and CNNs. Thoroughly enjoyed this course and would highly recommend to anyone who wants to learn how to prototype and build quick model POCs for your business objectives.

If you are a beginner to Machine Learning (ML), this is a great course that teaches you ML practically using TensorFlow. These assignments are easy and if you have prior ML knowledge they boosts your confidence in applying them using TensorFlow.

Loved the course, thank you! I do wish that the exercises emphasized testing validation more as opposed to just training validation. I'm hoping to learn how to use callbacks to cutoff training after a certain testing validation level is reached.