The course is good, you learn a lot, and I love the passion of the instructors. That said, I wish they'd taken some time (10-20 minutes) to explore/explain some basic ML concepts before delving right into the code at the start. Other than that, its good, and the assignments feel useful to complete, and what you learn is definitely interesting and inspiring. Good job!

That course was superb, but there were much references to other content, ik that was important but just as a suggestion I would say you can put this specialization in 5 courses or add 1 Week in each but please teach that all, by the way 4 stars were just to grab your attention as review otherwise I literally loved the content thank you Mr. Laurence and Mr, Andrew

Should probably spend more time on lectures. They're a little too short, and almost exclusively just designed to discuss snippets of code that help you solve the immediate assignment. I realize that this is a hands-on course, but intuition provided could have been much better. If you want to know how that's done, refer to 3Blue1Brown's series on Neural Networks!

very structured from the beginning, the time required to complete this course is calculated very carefully so even if you're a beginner with deep learning you'll be complete the course on time. there are a lot of useful resource links as well which reduces the learning effort. i really liked this course and it made me more confident to pursue the specialization.

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.

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.