Great practical course: if you have previous understanding of the Deep Learning models (for this course, multi layer perceptrons and convolutional neural network, for which I recommend to do before the Deep Learning specialization from deeplearning.ai) this course is very good for learning the practical implementations in the tensorflow2's sequential API. Great programming materials, videos and exercises.

I absolutely enjoyed this course. I liked the mix of lecture and TA/mentor time, the exercises done alongside our TAs/mentors were extremely instructive and helpful. The textbook recommendations were great. The weekly exercises were well-designed. I highly recommend this course, and I am looking forward to our next course in the sequence. Thank you for putting together such a fine class.

This is one of the best course I have taken in coursera ！！！ The course is very well designed. I have learned a lot about the sequential model in tf2.0, I also build the deep learning model by myself through the practice in this courser. And finally gather all of the knowledge in the final capstone project. I look the video lectures again after one month time, and understand it more clearly.

Initially I wanted to do only Probabilistic DL (3rd course) because this material is not taught anywhere else as far as I am aware, but I learned quite a bit from other two courses as well even though I thought I knew the material. The entire specialisation is highly recommended, very good quality and very relevant content. The best of 2020 on Coursera, in my estimation.

It was incredible experience to study TF 2 with this course. Progressively, we studied each component of Tensorflow to build eloquent ANNs. However, it was very shallow application of Tensorflow in just using CNN (which they explained 100%), it would be very good if they also showed some other architectures too.

Getting Started with TensorFlow 2 was a great course -- focused, relevant, professional, and highly value-added -- thank you, Dr. Kevin Webster, and the Graduate Teaching Assistants, for presenting it! I am looking forward to completing the next course in this Coursera specialization! Best Regards, Andrew

You need to have some experience in numpy before taking this course. In particular data preprocessing is challenging without such experience, I would say. Other than that, everything is excellent. Especially I like that I can run codes using CoLab/Jupyter notebook without installing all dependencies.

This course is a good complement to the courses offered by Deeplearning.ai in terms of focusing on the basic neural network coding. I like that the exercises in the Jupyter notebooks were left empty for students to type the codes. This encourages the students to pay attention the tutorial videos.

Intended for intermediate level students, is seriously one of the best courses with the right amount of rigour and testing. Thorough coverage of TF2; seriously would love more of such courses (apart from the specialisation) from Imperial College in this ever expanding field!

Great course, i loved the fact that instructors were showing the use of Docs and the assignments in the course were nice. I like that the couse assumes a prior knowledge of Deeplearning and does not repeat them in great details here.

I recommend this course:

1) A lot of practices: assignments, notebooks, capstone project.

2) Theory videos are very clear and compact.

3) Authors don't try to teach you ML in 5 weeks, but instead require ML knowledge and focus on TF.

Excellent introduction to Sequential models in Tensorflow 2, very clearly presented with well-designed tutorials, and covering a lot of useful material. Assumes a little (but only a little) previous knowledge of neural networks.

Such an amazing course! Explanations are concise and clear, and the labs are always a good opportunity to apply the new content. Weekly and final projects are great, and actually have a real application.

Great teachers as well!

Excellent course along with course#2. Very clear and useful explanations about using Tensorflow in a professional settings. These courses are real lifesavers given how terrible is Tensorflow documentation.

Good course. Short and very practical. It is not a basic course, as you need to know some aspects of Machine learning and Deep Learning. It opened my mind for other possibilities of use of neural networks.

Capstone project and assignments were much more hands on than other programs like DeepLearning. AI This is more useful for the industry. Other courses might be more beginner friendly but less practical

I already knew the subject, so I was able to go fast, but I really loved the completeness of this course, the approach, the tests, and the capstone project. Basically everything. Very good indeed!

Excellent course! The project assignment provides a very good way to self-assess and see whether you really have understood the course material. It's a strong recommendation from me!

One of the best courses that i have taken on coursera. Clearly explanation of concepts and very good labs which give data scientist clear path to train models using tensorflow 2

Excellent Course .. One of the best for practicing Tensorflow . Great content and well designed assignments ... GTA did a good job though sometimes the accent is not very clear

Very good training. I certainly learned a lot and am getting used to this framework already. Some theoretical background in ML is highly recommended before taking this course.

Working along with University PhD students is very helpful! Also, the explanations from Kevin are very detailed with good visualization (highlighted source code lines).

Awesome course for the students who wanted to start the TensorFlow. Instructors are best, explained the topic in a simple word using appropriate practical examples.

it is an awesome course for anyone with knowledge of deep learning to learn tensorflow. looking forward for other courses in the specialization to learn more.

This is a great course to gain experience using tensorflow 2 and also to reinforce the concepts of Convolutional Neural Networks. I strongly recommend it.