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.
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.
par Masoud V•
I have done the initial Deep learning courses of Andrew, and they were very thorough and well explained. I was expecting the same quality, however, it was not so. Explanations were generally good, but the examples and the details around the architecture of the models were barely discussed or considered, besides pointing me to the next course (which I have done). I was a bit disappointed TBH, for an "applied" course I do not think this provides enough material to begin applying this knowledge into real life problems.
par Joanne R•
Really poor quality, sadly. The notebooks are full of errors, the quizzes are mostly coding questions instead of being about deeper understanding of the notions studied, and I don't think the videos are clear enough about what decisions are most important when building this type of model and how to make those decisions. Love the topic, but very disappointed, and don't think this is worth what I'm paying..
par Andrei I•
The course is merely a walk-through some Jupiter notebooks of Laurence. There are no proper slides with explanation of what's going on. I also don't see much activity from the course creators on the discussion forums. It is incredibly easy to complete the course without forming any deep understanding.
The weekly programming exercises are not even automatically checked for accuracy.
par Praful G•
If you already have good knowledge of Neural Networks like CNN, RNN, LSTM, etc. then only opt for this one. Because they keep referring to previous courses in the specialisation for these. Also, they are only writing the code but never cleared about, what they are writing and why.
par Ebdulmomen A•
quiz's are pathetic! throughout the whole course the instructor talks about the advantages of RNN and LSTM and CNNs for time series prediction while not being able to prove this not even for one in the entire course, what a disappointment !
par Kaushal T•
The course was not as detailed or in a flow like I expected from a deeplearning.ai course and the editing was also very bad, one thing was shown and something else was spoken.
par Victor H•
A bit too high-level with lacking explanation on intuition. E.g. Conv1D was added to LSTM layers which helped reduce loss value, but did not go into the explanation of why.
par Tomek D•
Course is very quick and does not cover the topics in sufficient depth - explanations and discussion are all very brief.
par Akiva K S•
Junk course. Andrew Ng is a great specialist but I'll never try courses from deeplearning.ai.
par Yevhen D•
This course will be good only for very beginners. It's not deep and challenging enough.
par Sergey K•
To make it better you have to develop more challenging and GRADED! exercises
par Sujin S•
Poor audio quality.. Cant even hear in full volume
par Gabor S•
Very bad quizzes, no challenge whatsoever.
par Bojiang J•
Too much repetition in the content.
par Anant G•
It is a surface-level introduction
par Ankit G•
Could have been better
par Magdalena S•
par Adam F•
This specialization is false advertising. It does NOT prepare you for the Tensorflow certification exam. It’s especially disappointing after taking the fantastic specialization by Andrew Ng, and makes this specialization feel like a cheap cash grab by Coursera and DeepLearning.ai. This series of courses fails to prepare you for three reasons:
1 – The certification exam is done on whatever is the current version of Tensorflow (v2.6 as of writing). You can’t expect a specialization like this to update every minor release, but much of the coding is still on the v1.X version!
2 – The certification exam requires you to work in the PyCharm IDE. The IDE doesn’t even get a mention in this specialization and it is all done through Google Colab.
3 – The material is covered at a very superficial level. I was hoping to walk out of it feeling confident in using Tensorflow on novel problems, but I’ve barely learned anything about Tensorflow that I didn’t already get from Andrew Ng’s specialization. There’re a few minutes of lectures (some weeks less than 10 minutes). The programming assignments are either pathetically easy, or lack any guidance on what to do (seriously sometimes there’s no instructions at all, you have to guess what to do by the variable names), or both.
Save your time and money and go elsewhere to learn Tensorflow.
par Oliverio J S J•
Like the previous course, this is not a four-week course but a collection of short videos that can be watched in one morning. The first week (actually the first hour) provides interesting insights about time series but the rest of the weeks (hours) are a quick review of code snippets. The pace set by a sequence of one-minute videos is excruciating and makes it difficult to follow the lectures. In addition, there are no practical assignments, just some trivial quizzes.
par Xiaotian Z•
I do hope that the deeplearning.ai team could spend more time polishing the materials instead of just throwing the Tensorflow docs/sample codes and going through them superficially. Please also change the instructor as I really doubt his professionalism/experiences in ML practices despite his titles. Please, please don't ruin your brand, deeplearning.ai. I wish to see more in-depth courses like the ones taught by Andrew.
Maybe I had wrong expectations from this course. But to me it felt like the material in this course was extremely superficial. I was hoping to learn something, but it turned out to be a very basic overview of the material. Everything boiled down to "compile + fit" without the explanation of nuances associated with time-series settings.
par Brad N•
The last two parts of this 'specialization' were pretty much useless. Here's some code, let's look at the code three times, let's take a kindergarten quiz, let's look at the same code again, here's the answer you can copy if you bother doing the exercise.
par Yanghao W•
This is a quick introduction of using TensorFlow for prediction without any explanation for helping students understand the codes, the rationale, and the technical details we need to know for doing practice in daily work.