Introductory level course. Perhaps the best feature of this course is that you start to learn by doing right from week-1. The instructor doesn't spend time explaining every line one by one with every detail. That leaves you to figure out a few things on your own due to which you're bound to remember these details. Notebooks are also much better than the deep learning specilaisation. I often found myself going through the tensorflow documentation - learning and exploring as I did. Fun course!

Cours intéressant, clair et facile à comprendre. L'enseignant est très sympathique et semble passionné. C'est également facile à comprendre même pour un non english native speaker.

J'ai déjà suivit les cours d'Andrew Ng et comme de nombreuses interview d'Andrew et des références sont données dans ce cours cela les rends très complémentaire.

On a qu'une envie c'est de continuer pour en prendre plus sur ce sujet passionnant.

Je suis très satisfait de suivre cette spécialisation

Gilles Pierre

Perfect Course, It tells you how to implement basic neural network to deep cnn.

Week 1 : Intro to tensorflow/keras

Week 2: building dense neural network; 2 applications fashion mnist and digit mnist

week 3: building cnn, improving accuracy on week 2 applications

week 4: using image generator to auto label the images, horse/human classifier and happy/sad classifier

Overall a perfect course to begin with, though i would advice to have basic understanding of neural network and its working.

Although the concepts can be a bit technical, the explanations are very accessible. You learn how to implement ideas instantly, and by the end of the course, you are very well suited to experiment on your own. I'll be continuing the specialization. This, together with the deep learning specialization (which I have already taken) will I believe fully prepare anyone to work or do basic research in this field (at a fraction of the cost of a CS or AI undergraduate degree). Fantastic!

Very well structured course to get started with Deep Learning using TensorFlow. Laurence explains the flow of the programs and key concepts in a clear manner, which made it easy for a beginner like me to get familiar with the material quickly. Working through the problems without getting stuck in the theory was a fun way to see the possibilities of AI first hand and learn by doing. Highly recommend this course for any developers or programmers who want to explore deep learning.

This course should be considered as the inception of the Tensorflow when one steps into the field of deep learning. That would be very helpful to build a comprehensible network about this famous framework and how AI is applied to real life. I suggest introducing more practical work, for example, the video in the course, how AI helps the Africain farmers to know the health state of their crops. Using this case as an example might be more exciting from my point of view. Thanks.

The author, is way of teaching make me understand more into the fundamentals of the concepts. Now i have the strong understanding of these concepts with Good hands-on-experience.

But the current course i only audit, because of financial problem. If you provide me certificate for completion of these course it will be definitely useful for my career goal.

My leaning never going to stop , hereafter i will learn lot of courses from coursera with the financial aid.

Thanks.

Although while pursuing a higher degree in AI it was great to learn the minutiae of machine learning methods, this course seems to provide the best abstraction for a high level programmer - giving flexibility to play around with different datasets and GPU architectures and making it super intuitive and easy to apply it to your own problems. Let's build AI for solving problems where human decision making is limited It's really cool and you can make a difference!

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.

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.

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.

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

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.

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!

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.

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

This course is helpful to understand how to create basic deep learning networks and models using library TensorFlow. I understood the implementation of deep learning models using developed python library. But you should have basic knowledge of deep learning before starting the course otherwise you won't be able to understand fundamental concepts used in this course

I am a programmer with a little knowledge of statistics. Other courses really dive into the theory and I get lost pretty fast.

This one however just teach you how to use the existing AI libraries without digging deep into how they work and what algorithms they employ.

It was pretty easy for me to follow through with this course and I'd recommend it for any beginner

Fun introduction to tensorflow. This course is best if you completed the deep learning specialization before it this course doesn't go into the details of how everything is working under the hood.

The structure of the course is well done with plenty of opportunities to practice.

There are still a few rough edges to smooth out, but all around really great.

I have completed more than 80+ MOOC from various platforms and many reputed universities and I must say, Laurence is one of the best teacher(s) I came across. The way he teaches (which includes topic content, explanations, and converting complex concepts into simple one) is simply unparalleled.

A BIG THANKS to him and everyone associated with the course.

A very good start for tensorflow beginners and a concise refresher for those who have already used the tensorflow framework. Loved the way each code was explained but particularly found the part where we can play around with what each filter is learning as particularly important for anyone going forward with this course. Thanks for this great material.