I really enjoyed this course, especially because it combines all different components (DNN, CONV-NET, and RNN) together in one application. I look forward to taking more courses from deeplearning.ai.
Really like the focus on practical application and demonstrating the latest capability of TensorFlow. As mentioned in the course, it is a great compliment to Andrew Ng's Deep Learning Specialization.
By Asad K•
The first week has some interesting discussion of time series data and some traditional non-ML methods for forecasting, but beyond that the course quickly divulges the all too familiar weaknesses of this specialization; lack of depth, elementary discussion, weak insight into common problems that arise during training models, and extremely poorly written quizzes that don't test the learner's gain of knowledge or skills in any meaningful way.
My biggest complaint to the instructors and the team is that for months this specialization promised the last course will discuss the WaveNet model, but the course didn't even do a cursory survey of it (In week 4, the instructor adds a Conv1D layer but doesn't even discuss the causal padding and completely skips dilations, etc, so that in effect there isn't even discussion of a single layer from WaveNet model). Sigh !
By Fengjun W•
Finally, wasted my weekend and 40 euros to finish this shitty specialization. I really dont know the target audience of this specialization. If you have no background of deep learning, going through some code snippets without any explanation wont help you at all. you can't know anything behind it. If you already have some knowledge, you will find nothing new and more in this course. 1) The materials are so shallow and without any depth, just reading the slides and codes with errors. Only some high-level keras APIs are covered. The official tensorflow tutorial is much better. 2) The test questions are of no value at all, it cant test any your understanding whether about deep learning or the tool tensorflow. The assignments are poorly designed, the answers contains errors. 3) I strongly doubt the instructor, I think he does not have much ML experience. Please don't waste your money and time on this specialization. If you want to learn deep learning, go to cs230; cs231n for computer vision; cs224n and cs224u for NLP; cs20 for Tensorflow.
By Irina G•
Very weak course, shallow, lacks content. Can be "learned" in a few hours, not weeks. Really hoped to see a working ML model for a time sequence, but the examples shown in this course do not demonstrate why bother with ML. If these examples were middle-school home work, they would be graded D+(keep trying or better use other methods). The instructor doesn't come across as an experienced ML practitioner.
By Steve H•
Very superficial presentation of the material, and disappointing content given all the initial hype. Whatever happened to working with WaveNet? The 4 weeks to complete the course is a massive over-estimate. Expect to spend not more than a day going through the course. Quiz questions are very low value and do not test any understanding.
By Kaan A•
Unfortunately, These whole Specialization didnt match my expectations. I finished whole Deep Learning Specialization and I LOVED IT. Before starting this one I had very good feeling about this specialization; however I learned very little. Most of the videos are like "this code does this and this code does this and this line does this and this function does this etc. " . A bit disappointed, but still learned some.
By Jussi H•
I wanted to like this specialization, but I just cannot. My expectation was that this specialization would complement Andrew Ng's excellent Deep Learning specialization, but it does not. Whereas the DL specialization taught you best practices and a systematic approach to improving models, this specialization throws all of that out the window. The architectures are downright silly in some of the examples. If you want to learn TensorFlow, you would spend your time more wisely by working through the official TF tutorials, which are pretty good.
By Charlie C•
No concrete knowledge, no solid explanation. Just some demo.
By Matthias W•
I just finished the entire course on sequence learning within the trial period. I feel it was a waste of time: Hardly any new content, nothing that inspired me really beyond what I currently do. The quality of presentation IMHO was sub-par and of low-quality content. Laurence constantly stated how the MAE went down when it actually did not significantly at all. After a while, it sounded more like that he tried to influence the observer to believe that his model tweaks had a positive effect on the prediction accuracy when in reality it had hardly any effect at all. Also, at times I got the impression that Laurence did not really fully understand what he was doing, such as pushing up and down batch sizes, learning rates, flipping layer types back and forth. It did not appear to me that there was any thought process behind or logic applied to why he was doing what he was doing. Disappointed!!!
Quite disappointed about this sequence after the awesome other 3 courses taught by Andrew Ng.
By Debojyoti R•
Very bad course.
This course only explains time series at a very superficial level. The coding is done through Keras. The instructor does not explain the back-end. The course does not teach us about models like ARIMAX, ARIMA etc. Just some simple problems are solved in this course. Also, in the first three weeks, toy dataset was being used. The audio is also very low. There is no graded programming assignment. The quiz questions are also below standard.
I can't figure out the target audience of this course. The documentations and various blogs, books available in the internet has more content than this course, which can be completed in a few hours.
By Han L•
poorly designed course, easy material, lack of depth, shallow quizzes, lab exercises on colab, lab exercises is not reflective of current tensorflow version.
By David R•
It's a bit of an insult to call this thing a "course". The entire video material is maybe a bit over 1 hour for the entire "4 weeks". The quizzes (1 quiz per week) are ridiculously easy, and the (ungraded) exercises are basically - "do what we did in the lecture, only from memory".
The material itself is good, but doesn't go in depth. They introduce Huber loss, and then tell you to go read about it in Wikipedia.
Overall - low quality. Would have been great as a first week in a real "mini course" (DeepLearning specialization style), or one lecture, in a real academic course.
By Kirill S•
A lot of repetition of the same methods, no clear indication on how exactly to tune the chosen NNs (for instance, how to select their order, how to tune optimzers' parameters, etc) + extremely simple quizes. In general, it looks like this whole specialisation was designed just to earn some money on a existing deeplearning.ai brand. Huge disappointment.
By James B•
No graded exercises at the end of the practicals. Some of the quiz questions seems to be based more around general python and in 1 situation around the presenters only thoughts. Some information about estimating optimal learning rates was incorrect and misleading
By Mrinmoy S•
This course quality is so poor. Didn't understand in details. Are you kidding by explaining all of these topics in just 1 min long videos ! I won't recommend this specialization course at all.
By sherry g•
Not much to learn..very basic course without any explaination.. neither basics are cleared nor details are discussed. i didnt get idea of having such a course online and that too on coursera!
By Ojas R•
It was an amazing experience to learn from such great experts in the field and get a complete understanding of all the concepts involved and also get thorough understanding of the programming skills.
By Yaron K•
A step by step explanation of how to use TensorFlow 2.0 for building a Neural network for sequences and time series. With detailed examples of code and of how to choose hyper-parameters.
By Marghoob K•
This was really a beautifully designed course. They didn't focused on teaching too much of thing at once but build up the base slowly and strongly for better understanding.
By Parab N S•
An excellent course on Time Series and Sequences by Laurence Moroney. Explained how to use CNN, RNN and DNN together to bring the nest out of time series prediction.
By Subhadeep D•
Quite a good light-weighted course on Time Series and Prediction. It was quite helpful for people like me who are seeking ways to implement the concepts.
By Silviu M•
The material is great and the presentation elevated and professional. Few thoughts nonetheless: a) i know time series, came here for specific advice on how to tune models. I was extremely disappointed. Stationarity is mentioned at the very beginning but then it fades as if it was completely irrelevant to ML. b) there is more than one contradiction in the presentation. MAE is going up yet the presenter says that it got better??? That I think would be really confusing, particularly for novice learners. c) black boxes: I acknowledge that there are so many decisions and choices one needs to make when setting up a training model. Wouldn't it be relevant to highlight them and explain how different decisions impact the outcome? This course was failing on that.
By ALVARO M A N•
Personally I loved this course, I had a previous knowledge of this topic, because it's one of my favorites topics (very related to IoT analysis data). And here I've learned various top technics suchs lambda layers, or that we have to split in training, validation and testing periods the data. This is something that you don't see in many books or manual about time series with tensorflow. And finally I've learned very useful libraries that I even didn't know that exists like tf.keras.dataset, that makes so easy to give format to the data, before you had to write more code. So with this information I can write more effective and efficient code! Thanks Laurence and Andrew from Perú!
By Richard S•
This course was my ultimate motivator and goal for taking the specialization as I am doing work with time series. Very interesting to learn a traditional statistical approach, then apply DNNs, RNNs, LSTMs and CNNs to time series prediction. Even though just scratching the surface, I can apply knowledge from this course and specialization immediately.
Thank you Laurence and Andrew for a fantastic course and specialization! I am inspired and motivated to dig deeper into the theory of NNs and their application with further courses and projects.
By Saif H•
It was a brilliant course , I thoroughly enjoyed learning various aspects and techniques of Deep Learning techniques and in the process also learned a lot about TensorFlow . As mentioned by LM , its the first step and I'm really to have taken that first step.
One of the issue with the course has been the quality of audio, all the other course I have done on Coursera had very clear and audible voice over , however with this course I have struggled to hear with the audio, hopefully this can be addressed in future course.
By Hannan S•
First of all, the course was amazing! I found it great for the following reasons:
- Laurence Moroney (Instructor) was very professional and clear while delivering the knowledge
- The introductions by Andrew NG were really nice
- Easy to understand codes and understanding of thr underlying principles
- Varied topics such as CNN, NLP & Time Series
- Very insightful by providing expert opinions about different ways of model optimization
I really enjoyed the course and I thank the instructor for the same :)