without a doubt, Laurence's teaching is much better than reading the documentation! The course is a great starting point!

Some of the material is a little confusing: sometimes the exercises will open on Google Collab, others in a classic Jupyter Notebook; instructions sometimes seem to lack revision to make them more organized and less convoluted. Also, references in videos should be always made into links (e.g. Andrew's videos are usually referenced as links in the class video and they are not clickable. Since the graded course is paid (and is not cheap for most third world countries) it seems that a little more care and polish should be applied. The contents are excellent, but they lack the organization and quality of the original course from Professor Andrew Ng, which was, by the way, 100% free and didn't have exercise grading locked by payment, for example.

I just can say that it was an awesome course. The instructors as well as the contents were clear, easy to understand and everything with a focus on how to take the theory and apply it with TensorFlow.

this course was helpful for me to learn more about CNN. it starts from basic concepts and improve that slowly and step by step. i truthfully recommend how what to start working with neural networks.

The assignments need to be polished. They were very lackluster and non-rewarding.

I get that this is a separate course from Ng's deep learning course, but I found the lack of theory (or even recommendations of best practices) disturbing. Additionally, I thought the videos were way too short and would have appreciated it if they had gone into detail into each Keras method used, the parameters for it, etc. For example, on the last assignment we were supposed to use a callback on accuracy to end training, but nowhere in the videos did it mention how fit_generator() handles callbacks as opposed to how they were handled with fit().

Lastly, and most importantly, this course was advertised to be a course on Tensorflow. However, this is not the case. This is a course on Keras; Tensorflow's API. If you came here looking for how to implement a DL algorithm from scratch in TF, this is not the course for you (or me apparently).

The notebooks do not provide enough information about what block implements what. A simple comment that implement custom callback here would have made things lot easier.

This course is generally one of high production values and the lecturer is of course excellent. Having taken Andrew Ng's original machine-learning course, it was as ever encouraging to see him make some short guest appearances.

The material and exercises are of a high standard apart from the usual typos etc. However, one particular problem was that some/all of the exercises appeared to be assessed via their print statements rather than by server-side via unit tests. This is frustrating and puts one in danger of seeing other people's code when attempting to search online for an unexpected/trial-and-error fix. Also, the timings are not quite right - some reading material comprises a few lines but apparently should take 10 minutes etc.

My main reason for undertaking the course was as a more practical prerequisite for the TensorFlow: Data and Deployment course offered by deeplearning.ai, my intention being to circle back round for the Deep Learning specialization afterwards.

Unfortunately, in spite of the positives above, the course really falls short in terms of value for money: for £45 one only learns of order 100 lines of code. This is positioned as TensorFlow having a nice, high-level API, but in practice the course is disappointingly straightforward for anyone who must have already demonstrated strong python skills and/or who are developers. There is no theory but the course certainly does not give practical "mastery" of TensorFlow.

Perhaps it was naive to expect better value, but it is hard to assess the course level purely from the syllabus. One can "audit" a course for free and I would recommend people do this in future - I certainly will be.

Overall I am quite disappointed. I look forward to the other courses in the Specialization which I am now undertaking purely in order to obtain better value for money.

I am not very satisfied with the course. It does feel quite professionally made, but there is no depth. It feels as if the teachers of the course had some difficulty when deciding on the prerequisites. I think it would have been clearer if the course would just have said: take this course after the deep learning specialization because this will build on knowledge from previous courses. Then, focus ONLY on teaching the coding part, explain what is TF, what is Keras, in DEPTH. For example, all the quizzes are more theorethical questions: these should be ALL code in TensorFlow. E.g, what is the following code doing? I guess it's just the first course, so depth is not expected but what I've read so far, it wont change in the following courses. I'm dissapointed and Andrew you should set a higher standard for your courses. Hope you will take lessons and not let this happen in future courses, I wasted my money and time on this.

This series of courses is just a 'Hello World' introduction of Tensorflow/Keras. The instructor just touches the surface of some code from the Tensorflow document without explaining some really fundamental concepts (e.g. tensors). The videos are usually 1-2 min long, really a headache to watch. The quiz is too simple and poorly designed-- instead of thinking or calculating you just need to remember some basic concepts/grammar rules. Programming exercises are not really useful and there is too much duplicate work. Not worth the money if you plan to pay for it-- auditing is enough. I am disappointed by deeplearning.ai for producing such a shallow course.

This course does an impressive job in filling the gap between theory and practice. It's part of a specialization and as such you cannot expect it to cover every single detail from the very beginning, and it's not supposed to go too deep into what Tensorflow can do.

It does an excellent job in introducing little by little the tools necessary to build neural networks, explaining the details of every line of code used and giving you everything you need to create a functioning, simple prototype.

It can't be stressed enough that this is an intermediate course and it obviously requires knowledge on the topic as well as programming in general, but it's not advanced. It's not supposed to explain the theory behind Deep Learning. It goes even as far as adding links to various free resources such as the youtube videos from Andrew Ng's Machine Learning and Deep Learning videos to help the user understand the theory behind some concepts.

This course jumps right in and in a clear way presents a few real world like problems and solutions for them using neural networks. I am not sure how I would have felt if I had no prior ML experience taking this, it's missing a lot of basics. But perhaps that's the positive, as you get to do coding and see things in action which hopefully should energize you to dig in a bit more into things like optimizers, gradient descent, neurons, weights, training, etc. Anyway, 5 stars due to good practice examples.

I completed the 5 course Deep Learning Specialization before and was a bit unsure, if this course would still be worth it - it absolutely was. It helped me augment the theory and limited practical experience I had so far with some fun, straight to the point hints, recaps and exercises. I particularly appreciated playing with the visualization of what goes on as an image gets transformed on its way through a convnet. That was super useful to deepen the intuition.

1 week into the course so far and I really, really enjoy this course. Been following Laurence for quite a long time now and more than happy to see two of the coolest guys in AI doing such an amazing work. Although I have exams I am not missing a day watching at least 1-2 videos of them :) 100% recommended!

it is a very excellent course introducing some basic knowledge about TensorFlow , teaching students how to use TensorFlow to deal with some AI programs. This course provides students with access to try their effort to use TensorFlow, and obtain more understanding about this power python library.

This is extremely gentle intro to TensorFlow. Additionally the course provides a lot of literature including free version of Deep Learning Specialization & Machine Learning course on YT. You can easily finish it in one week, it is not time consuming at all.

It is very well designed course,instructors teaches very well,i can understand all concepts and how to solve real problems using using CNN ,want such more projects based on hands on practice ,thanks

I would say that I have dabbled in Python and ML before taking this course. I even finished the IBM Data Science Professional Certification. I was very much able to grasp what was being taught here.

My first course on TEnsor and i have nothing to say but this course is a must for everyone who wants to know more about Tensor. It establishes a strong background one could lay advanced knowledge on

Es un excelente curso para todo aquel que este interesado en las ciencias de datos y la inteligencia artificial ya que Tensorflow y Keras son herrameintas que acortan mucho el camino de desarrollo.

It was great course to start hands-on in TensorFlow and convolution neural networks. I especially liked how the convolution feature viewing tool was used to see what the networks are learning.

Simply Amazing Laurence! Please keep me posted on more content!!!! this is the best person to learn from he just has a way that clicks for me I hope anyone else that is taking this course!!!!

As an introductory course, it filled my expectations. Short, focused videos, along with detailed notebooks. Can't wait to head on to the next course

The course is wonderful. I happened to learn a lot. Anyone with basic skills can move on with this course pretty much easily. Highly recommended.

The combination of theory & practical assignments helps us in learning concepts as a beginner, and have a good learning graph.