This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow. The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models. The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for any application. You will use lower level APIs in TensorFlow to develop complex model architectures, fully customised layers, and a flexible data workflow. You will also expand your knowledge of the TensorFlow APIs to include sequence models. The final course specialises in the increasingly important probabilistic approach to deep learning. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library. Prerequisite knowledge for this Specialization is python 3, general machine learning and deep learning concepts, and a solid foundation in probability and statistics (especially for course 3).
The course was very well planned and the lessons reflect that good foundation. The teacher is very professional, empathetic and polite. I really enjoyed taking this Course ??
Great Course, Got a lot to learn. Few things can be presented well especially in the 3rd the 4th week lab. Rest everything is good.
Very good. Liked this course a lot, even though I recognize I should have had a better a background before taking it.
Really excellent course and quality of lectures and coding tutorials were beyond my expectation. I think this course is literally the best TF course available in Coursera
Great course, really helped me getting a much more insightful view to an important package.
A very hard course but I leared a lot from it. Thanks Coursera and the great teachers
Amazing course, one of the best so far. The course discusses the Tensorflow 2 framework in a very detailed and practical way. Thanks for the opportunity!
The lectures are clear and the coding assignments are very relevant and practical. The final project is complex but it is very rewarding once you complete it.
The course is very hard but too important and interesting.
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
Great follow up for the first course by going deeper to Tensorf Flow 2.0
A really valuable learning experience. With these courses, I now feel confident that I can apply the skills from the Deep Learning Specialization in a practical setting.
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This class is excelent for beginers to learn tf2. this class mainly talked about practice ,and it is useful to build basic deeplearning models
Decent quality content, but course doesn't seem to really be supported anymore, and it isn't noted anywhere. I just found out when I couldn't submit the final project (was able to submit everything else). This should be noted in big red letters somewhere or something in case people are looking for a certificate.
Never recieved my grade. Contacted coursera support. They demanded another motnh of payment for the course or else I will not get my grade. Terrible practice and this is used to punish people that finish their course early since coursera will simply wait until you pay another month before the start grading your paper!
Course is very well designed and very well for learning tensorflow higher level api's. Peer graded assignment was very frustrating as it will take so much time to reflect that you are passed or not. Happy Learning!!
Awesome content, I learned A LOT from this course but encountered numerous technical issues with the Capstone project -- including not being able to generate the PDF via the instructions.
The course is really well-organized. The videos are relatively short but very clear and concise. Programming assignments are right on spot. If you already have basic knowledge of deep learning and want to get more practical experience with Tensorflow 2, this s absolutely a good resource.
Very nice introduction to tensorflow 2.0 with focus on keras. I already knew keras from tensorflow 1 so this was very useful. Still I thought this course was a little too basic for me to get a certificate. I used it as preparation for the other course ('customizing your models with tensorflow 2'). Doing this course quickly at least gives me a good background to do the second one.
There were some small errors in the course. It looks like tensorflow has adapted their APIs so that in the model checkpointing you specify the number of batches and not the number of samples. Also, there is a current bug in tensorflow 2.2.0 (and 2.3.0), that specifying {batch} in the path of the checkpoint no longer works. Also, there is another bug in tensorflow 2.3.0 that has apparently broken saving and loading of complete models using model.save() and load_model().
Going to do the second course now with the aim to get the certificate.
I think it is an absolutely incredible course for anyone who wants to make the jumpy from TF 1.x to TF 2 (or has the knowledge of Deep Learning ideas and is looking to learn the TF 2 framework to apply them). The content is planned out perfectly for learners, the tutorials and assignment introduce and understand important ideas from the tensorflow 2 framework.
Finally, the most amazing thing about this course is the Capstone project. It just strikes the perfect balance on the difficulty scale. It will keep you engaged but at the same time it will not be very difficult either. The content in the course is geared towards preparing the learner to be able to do the project with ease and it does serve as a culmination of the skills used learnt in the course.
This is a truly outstanding course that teaches you the basics of Tensorflow 2 using the Keras API. (There is a more advanced course if you want to deep dive into Tensorflow2)
One minor quirk is that instructors almost never answer questions on the forum. Nonetheless, it is worth the money spent if you plan to start designing Neural Networks with Tensorflow 2 framework.
The Capstone project is really satisfying since it gives you a lot of flexibility on how you can to complete it.
This is such an amazing course. It's very simple but accomplishes what it's supposed to accomplish. I've taken the andrew NG course for deep learning so I know the theory and this finally made me understand what I've been writing and allowed me to express some of what I think in terms of code instead of only having the theoretical model. and even more stuff that the other course didn't cover in terms of tensorflow
A good course for beginners in TF, though familiarity with deep learning is assumed. The course is up to date with TF2 API, so I had no issues implementing the excercises. The peer-reviewed project is open-ended and gives a lot of freedom to build the appropriate network architecture. The instructors are clear and overall production quality is really high.
Excellent course with thorough practical exercises and most of all I love Kevin Webster teaching style.. Definitely a go to course for anyone who has some basic Deep Learning knowlegde.
A beautifully structured course. It does require a fair bit of preliminary knowledge in DL, but once you have that, the practical implementation in this course is on point.
A great course for those who already have some knowledge of deep learning. Concepts are explained in very well way.
Well designed course, with the final Capstone project covering all the aspects involved in a MLP.
Very good course but the final peer graded review takes alot of time
Excellent course with videos and assignment.
First of all, this course is not a deep learning course but a TensorFlow2 course, so prior knowledge of deep learning is required. For example, if you don't understand what CNN (Convolutional Neural Network) is, it's recommended to take a dedicated deep learning course, such as OpenAI's, before starting this course. With that in mind, the content of this course is excellent. You will become proficient in executing what you imagine using TensorFlow2. However, there are some drawbacks. Almost every module consists of the following components: 1. Videos where the instructor explains concepts while showing code. 2. Videos where the TA (Teaching Assistant) explains concepts while coding. 3. JupyterNotebook exercises. 1 and 2 are quite redundant. Moreover, video 2 involves typing out code from scratch instead of explaining pre-written code, which makes the videos unnecessarily long. Typing out code while watching the video was quite painful. In addition, the lab's automatic grading tool was a bit unstable. Particularly, during the second week's auto-grading, it would fail when the lab was working correctly, and pass when errors occurred while coding as per the instructions on the lab. Additionally, there were issues with the peer review assignment, as attempting to generate a PDF as instructed resulted in a server-side error, preventing the output. I downloaded the Jupyter project and struggled to generate a PDF from my local JupyterLab.
The course material is fine but the peer review thing must really be get rid of. I don't wanna see the discussion forum filled with people begging for reviews.
Something seemed off with the course, there was a quiz that covered information I'd never seen in the videos. They breezed right through very important foundational concepts, assuming you already knew about them.