This course was very useful, especially if you gone through the deep learning specialization, which I did. The course starts with implementing a simple neural network, and then progresses onto convolutional neural networks. The course ends with a very useful data management topic on training data labelling. I would have found it useful if the course also focused on alternative attributes for the some of layers like Conv2D, the compiler, etc.

Very well-balanced presentation. Started considerable fluency in python, advanced education in digital-signal processing, but limited experience with ANNs in general and tensor-flow in particular. With this course — and some background in Dreyfus 2005 and Haykin 2009 — I feel like I'm starting to get the picture. The hands-on was straightforwardly presented and extensible, with just enough wrinkles to make you think and stump the chumps.

This was a great course to learn how to use the basics of Tensorflow. The layers mentioned in the course are Dense (fully-connected layers), Convolutional, Pooling and Flatten layers. It also teaches you how to use callback functions during the training phase, as well as how to handle real-world images.

This course does not teach you about how to tune hyperparameters, but I wouldn't expect that from an introduction course.

Great practical course. I think that if you don't have the background of Deep Learning (that is given in other Deeplearinng.ai specialization, the Deep Learning one, which I really recommend), is better to understand first the concepts before doing this course. But if you have the concepts, and understand how this NNs work, this is a great place to learn how to put in practice all this with the Tensorflow's Keras API.

Using https://colab.research.google.com/ is definitely a good choice because it saves the learners a big chunk of time setting up a deeplearning workstation by herself. The course's source code is commented extensively to ease understanding. Although the technical details and specific explanation of the deeplearning algorithms are pretty lightweight, it's more than enough to get one's hands dirty as a beginner course.

Yes of course you should know the basics of the Deep Learning or Machine Learning to completely understand this Course. I recommend to first enrol in Andrew Ng Course (DL or ML Specialization) and then enrol in this course.

Personally I liked how Laurence started with very small examples of how to classify clothes using only a very simple Neural Network and then improved the accuracy with the CNN.

Thank you Laurence :)

I really enjoyed the line-by-line code explanation and the right balance between theoretical and practical parts. As a beginner, it is easy to get lost in all different types of layers, optimisers, etc, and this course helps understand a general structure of a neural network program. The Python notebooks that we are given are extremely useful for watching how the things work by experimenting with code. Thank you!

This is an amazing course, and one can feel the hard work they have put into it. I was able to experience so much theory in practice, image augmentation, dropouts, transfer learning. Learning experience? Much better, it was a learning enthrallment! This is one of those courses which make deep learning looks so easy, and approachable. Highly recommended for anybody, and coursera should have more courses like this.

The course contains a lot of resources and intuitive knowledge for deep learning. They also provide good exercises for demonstrating the idea the instructor tries to convey.

However, I think we need more hints or instruction on assignment. Many times, we  got grader errors or some other glitches.We willneed to check the discussion forum if there is any one faced the same problem and a solution or not.

Very interesting start to TensorFlow. This course although doesn't teach the basics of a ML model (which Stanford's ML course and the Deep Learning Specialization already do in great detail) but gets right down to how you can use TensorFlow for classifying what type of problem such as the basic regression problem to an image classifier. This course offers intuitive on how to properly use TensorFlow.

Enjoyed the visualization part where we were encouraged to peek at different parts of the neural networks to understand how features were created.

Could be somewhat more thorough on how different amount of layers and architectures affect the quality of features created and the overall performance instead of encouraging people to just play around.

Useful, to the point and easily digestible intro to TF

This course is the first course of TensorFlow Developer Specialization. The course I intended to enroll in was the second course. I took this course as a warm-up before getting into the second. However, it was worth my time. It contains everything you need to know and teaches essential points that are easy to ignore. I absolutely recommend taking this course, even if you are not a complete novice.

I loved how this course is structured. I'm right now preparing for my interviews and this course had concepts explained in like awesome way that you could use to answer in interviews. The google colab platform was very helpful and tutor Prof.Lawrence was incredible at explaining the codes line by line. On the whole, I would go over this course over and over again to get my concepts at fingertips.

Awesome experience. It's an amazing course for those who want to explore Deep Learning and Neural Networks. It was a wonderful experience of creating Deep Learning models with real life datasets. I have leant from basic and understood each and every lesson. Well designed curriculum.

Kudos to Dr. Andrew Ng and Course Instructor Dr. Laurence Moroney. Thank you so much Coursera & DeepLearning.AI

If you just make this Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning Course you can manage to make a few simple codes.

This introduction complements very well with Deep Learning Specialization. In case you have done the specialization, this course will give you the first steps so you can start programming and learning for more complex applications.

Great course for starting the implementation of classification model on Keras. Although more detail into theoretical part should be found from other courses or materials, this course definitely provides the very first step in the coding part. For me, with little prior knowledge in python, I did learn a lot by studying the Notebook sample code carefully. So I will recommend this course!

If you're looking for a more theoretical approach, go take Andrew Ng's Deep Learning Specialization first. This course is more of a crash course based from what Andrew has already discussed, and is heavily focused on hands-on application using Python. The course will encourage you to experiment and understand the impact of different parameters in your model. It's fun and challenging.

The learning process is very explanatory and very easy to understand. the course has many quizzes and programmable problems which allows the learner to get a clear view of how to work with TensorFlow. the course also provides various notebooks where you can experiment by changing the values of variables and see the effect of it in the output which in turn makes the learning valuable.

I am planning on taking TensorFlow certificate, so I finished this course in 5 days. Glad I did that as I did lean how deep neural nets and convolutional neural nets work! I am still in the beginning of my path, so I will keep working hard to finish all of the other courses in order to achieve TensorFlow certificate. Thank you, Laurance and Andrew, for the amazing beneficial course!

Elegant and efficient introduction to the plug-and-play Google Colaboration application that let's you easily write and execute Python code in the cloud, as well as the Keras API that let's you access the Tensorflow package to use with convolution and pooling layers. The exercises were brilliant as well and I have high expectations about the following courses in the specialization.

A quiet interesting course that introduce you to TensorFlow, and gives a basic understanting of image processing in Python. The course was well organized with explanatory videos, code scripts in google collabs, and necessary assignments to practise your code skills. Overall I am very pleased that I attended this course, and I am planning to attend more like this in the near future

Concise and cohesive course, and was very easy to follow! It was nice to see both the theoretical and practical concepts explained in a manner that a beginner such as myself can understand. They provide many other resources in case I get stuck or need further explanation as well. Thank you for a great introductory course, and I am excited to learn more in the future courses.

Great intro by Laurence, very comprehensible. But i think a bit more work should have been done on presentations for deep learning newbies for them to be able to grasp the concepts of difficult topics like convolutions.

Though i would recommend anyone to first complete Andrew Ng's deep learning.ai specialization first, then start this course, it would be really beneficial.

I think this is a great introduction to Tensorflow 2.0 as a whole. What other reviews may say is that they didn't go into much of the theoretical aspects, yeah that's fine after all this is a course that is meant to be practical by all means. For people who've already taken Andrew's Deep Learning Course for example.

I think the instructor has done a great job! 5 stars!

Liked the course as it did not spoon feed you the answers and you had to go back and watch the videos again to attempt the programming assignments. Overall it was fun learning tensorflow. I am very excited for the next course in the specialization. I would encourage people to do the entire specialization because this course alone wont teach you much about tensorflow