The course topics were well conversed by Lawrence. But I find it difficult to process the programming assignment, not because its difficult for me, I completed what I am supposed to do and got desired output on it but the submission and grading mechanism is a bit faulty and misleading. The Discussion Forums also got not much mentors or fellow students helping

Really nice course for basic Tensorflow, It will be much better if a real world example is explained with every aspect of machine learning. Like data aquisition, sufficiency, labelling, model building, evaluation and deployment. I am not saying to show the steps how to do it, but some pointers will be nice. Final project should at least include everything.

A really simple and intuitive approach to Neural Networks in TensorFlow. Smart and simple examples to experiment by yourself how to build image classification with a few lines of code in python. For me was useful to recap what I already studied in DeepLearning.ai courses. This course was pretty good to do some practical exercises and an overall recap.

The Course has a good starter pack of essential topics. Loved the course as it had so much to teach me, and added to that, Dr Moroney made the concepts truly clear and easy to understand. The only part where I felt that the course could be better were the assignments. The difficulty could have been higher to make it more challenging to the students.

This is way too easy for an intermediate course I believe. But some of the concepts like visualizing the activations of the layers, writing the custome callbacks are really nice. Overall the explanations were really good and the notebooks gives really good insight and playground to play with parameters and learn how these functions (API) work.

I like Mr Moroney's videos. He is very good in explaining things. Sometimes he goes a bit too fast through the notebooks. It would be very nice if he could dive a bit deeper into the mathematical/theoretical backgrounds. The theoretical sources for reading are good, but I find it sometimes very helpful if somebody is explaining it. Thanks.

Great intro to getting started with TensorFlow. Highly recommended to do this after getting the basic theory from the deeplearning specialisation (i'mm the kind of person that likes to understand at least at high level what is going on so would say do the deeplearning first. However if you prefer to do the learn after could do it second!)

This course focuses on the practical programming aspects of deep learning networks rather than the theory. It is very helpful to get hands-on training with tensor-flow. I would highly advise taking Andrew Ng's Deep Learning course before taking this course as it makes understanding fundamentals of deep learning more meaningful and fun.

Very good to start some coding and get hands-on experience. However, they do not go into depth about what the parameters mean and why it makes sense to set them just as they do it in the instructions. It gives you a great experience, but I am not sure if all this is useful when working with real data on real problems with real pitfalls.

The course has a lot of good material, and is a great follow-up to the more theoretical Deep Learning Specialization.

However, I can't give it five stars because the exercises are a bit repetitive, and the quizzes have some very poorly worded questions. I know this is a new course, so I hope they can smooth out some of these rough edges.

Excellent course to get a grasp on the basics of ML/DL with Computer Vision on TensorFlow. Lecture delivery is super clear and the exercises nicely complement the material and give you the hands-on work you need. Having taken Andrew Ng's Machine Learning course will significantly help as this course is more practical than theoretical.

Good starting point to learn about training and deploying Deep Learning Models using Tensorflow. Some concepts cause a little confusion regarding the import structure of these libraries. Sometimes only import tensorflow is used while sometimes keras is imported as well. But all the code uses tf.keras , so it gets a little confusing.

It's good enough for beginners, but I have to say it's still a little bit easy. Maybe teacher can give us more reading materials or show us more interesting projects that we can reach after this Specialization and I think that can be called an introduction. But anyway, I think the course is better than many tutorials on the Internet.

Overall the course is very good . It gives a simple introduction to Deep Learning especially ConvNets. I would have loved if some of the concepts like Convolution were explained in more depth using some cool animations. The good thing about this course is that it never appeared to be a burden , rather it was a joyful experience.

This course offers a very solid introduction in tensorflow for CNN applications. One comment I had is that the quizzes did not test for deep understanding at all. They were more things you'd remember literally from the text and explanation, rather than new questions that can only be answered with some deeper understanding.

I'd already done the Deep Learning Specialisation which I think is important to do before this course. This one is a good chance to practice a bit and examine in more detail the syntax of Tensorflow. It's pretty good but it does jump right into to applications with not much of an overview (for example what is a Tensor?)

It's really a good course for hands-on in Deep learning using TensorFlow and Keras.

It's going to be great fun with very short videos and getting more focus on practical aspects.

This course is very precise and I really liked it and recommend for everyone who have their interest in AI, Machine Learning, and Deep Learning.

With this course I learned new things on tensorflow. However, It feels like it is very very simple introduction to tensorflow. After finishing Deep Learning Specialization I thought that this Specialization will be complementary. I'm disappointed a little bit on the first course. I believe other courses will be better!

Me gustó el curso, muy explicativo y lleno de recursos, aunque solo rasca la superficial. Hay muchas cosas mas por aprender. Los foros no reciben respuesta tan rápida. Si bien es cierto google Colab, ayuda a esta tareas, cuando se quiere pasar al computadora personal se complica por versiones o librerías faltantes.

Completed course - most issues I ran into involved submission related issues as I was running everything alongside on my machine and was successful. Part 4 also seems a little lacking in the middle for hands on time compared to the other sections, but I was able to figure it out by taking notes during the videos.

Course is good and provides a gentle introduction without getting into difficult details.

My only complaint is the volume of audio in every lecture is terribly low. Without a headphone, I am unable to hear the instructor even if I sit in very quiet room, with my laptop volume at max AND the video volume at MAX.

Very, very basic course for absolute beginners. It makes sure you know enough to build and train models for simple image recognition tasks. 4 weeks is a crazy long period for it, though. I finished it in 2.5 days (I have previous exposure though), but even a beginner should be able to do it in 1 week.

It is quite a good course. But you need to finish the additional materials along the way to really understand the purpose of parameters in different sequential layers. But that's only if have never studied DL. If you have, then like me you can rather focus on the computational features of tensorflow.

This course although thought me the basics of DNN but there can be improvements in the code (notebooks) and in the videos as they only tell the abstract part of the concepts. Overall the course was great it helped me alot from knowing what is keras to the journey of building my own model. Thanks

I expected that they would teach "Tensorflow" not Keras(its high level implementation), If youre here for learning tensorflow then maybe you should refer to some books. Overall the content is good if are new to Deep Learning and want to learn keras. Thanks Andrew-ng Thankyou Laurence Moroney.