The lectures covers lots of SOTA deep learning algorithms and the lectures are well-designed and easy to understand. The programming assignment is really good to enhance the understanding of lectures.
I was really happy because I could learn deep learning from Andrew Ng.\n\nThe lectures were fantastic and amazing.\n\nI was able to catch really important concepts of sequence models.\n\nThanks a lot!
by Mihir T•
A great course on latest technology used in NLP. The course is well structured and provides an in-depth knowledge on sequence models. This course is a all-in-one package for starting your career in NLP. Mr. Andrew is a great teacher, and explains everything in a very simple yet effective way.
by Wesley H•
Great finish to the specialisation. I have learned a lot of the core details of how to proceed with my own Deep Learning projects. My one piece of feedback would be for an intermediary step, that requires more of the programming myself, as a lot of the intricate coding has already been done.
by Challa S•
The course content is very good but the mistakes in the videos are being mentioned after the video. This is making us get confused a bit. It would be good if those errors are mentioned before the video itself so that we can look into that before watching the video and get prepared for that.
by Isaac S J C•
Great appreciation to Dr. Andrew Ng. The course has been incredibly well taught. Thank you so much for your enlightening lectures. I very much enjoyed the course, and I think it is very well structured and organized. The forum was very helpful when I got stuck in the programming exercises.
by Anujay S•
I am amazed with the learning experience of Seq2Seq Modules created by deeplearning.ai team! Loved the way it's taught by Andrew Ng and the hands on experience helped the mentee very well. Keep building such courses, would like to contribute more in this space as in research or products.
by Kyle L•
Insightful detail on model architectures and how they influence (and are influenced by) data generation for sequence-based applications. For those that have grasped the theory behind DNNs and are interested in applying ML to language and text, I highly recommend checking out this course!
by Abdulsalam A•
I like the course. It's beneficial and clear. Also, the concept is clear.
for more improvement
I would suggest that for jupyter implementation :
I hope you put 2 versions of the code
thus, the student can have a choice to work on a famous frame
1- using Tensorflow (TF)
2- using PyTorch
by Kumar S•
This course was really awsome,learning has been fun in all the 4 courses, the number of new things learnt in this course was remarkable.Even the mot complicated things were taught in such a way that it never seemed tough.Doing assignments really helped to make concepts even more clear.
by Lee F•
Fantastic course! Presents both the theory and practical uses in a straightforward manner that is easy to grasp. Programming assignments are a mix of NumPy and Keras API, with the former being more illustrative of the inner workings of RNNs and the latter being more practically useful.
by Nicolas C•
Excellent! Amazing! Such good quality of lecture and assignments. Thank you Andrew and team for giving me such a good overview of what i can use this for. I feel as though this series dramatically lowered the barriers to entry for me to get started on any ML project i decide to. Thanks
by Manmohan K•
No better introductory material. I suggest doing NLP specialization by deeplearning.ai after this though I have still not tried it out myself yet but hoping to do it some time. Thank you Andrew! I got emotional in your last video of the course. You are such an example for educators <3
by Aman K•
This was by far the Best Course and Specialization that I have done. Thank You Coursera and Thank You Sir Andrew NG . You have made me confident and able in the Field of Deep Learning. I am grateful to you Sir. I will try my best to use this knowledge as a superpower in the right way.
by Leonardo E T C•
In my opinion, this module was a little more complex than the previous ones, however, it has been an excellent course to deepen my knowledge in recurrent neural networks and close this specialization program in deep learning. Thank you very much, Dr. Andrew Ng and deeplearningai!!!!
by Liyan X•
Great course with interesting exercises. One can really see the amount of effort being put into creating the assignment material, so they are at a suitable level and with a lot to take away, and the students have a good understanding of details through practice. Really appreciated.
by Yirong Z•
I don't know how to express my feeling of gratitude. The DeepLearning Specialisation to some degreee, changes my life, including career and reserach. I will definitely make fully use of this superpower to improve humanity and the whole community! Thank you again to deeplearning.ai!
by anand k•
the programing assignment ins WEEK 1 was a bit ambiguous in nature. helped me improve my debugging skills.
Also a huge thank you to MENTOR Mr. GEOFF for the instant support to all my queries. His way of providing HINTS lead me to finally complete the course. a BIG thank you to him.
by Manhal R•
More easier to understand than the ConvNets course!
Week 1 and 2 took me a little time to get through. Week 3 is easy.
For better understanding, don't forget to download the notebooks and practice on your own local Jupyter notebook while using the assignment notebook as a reference.
by Jayash K•
This is a great course. It provides a good introduction to RNNs and how they are used in sequence modeling. Introduction to GRU, LSTMs, BiRNNs, attention model is great way to learn these in depth. The exercises are designed to make you familiar with the internals of these layers.
by Dong Z•
At the beginning this is very counter-intuitive. But later on when I am on the final assignment, I finally realized that we are not focusing on gradient descent, but architecture building and training set assembling! When everything start to make sense, it is really intriguing.
by Kyung-Hoon K•
This course made me have great understanding around the Sequence Models such as GRU, LSTM, Attention, etc. I had a lot of fun while completing programming exercises such as Trigger word detection. As always, it was one of the best class ever. Thanks Professor Andrew Ng and all-!
by Vladimir B•
Very good course. In quite short time you get understanding of a lot of principles and intuitions. The pace is good, explanations are consistent and clear, top-down approach from generic to specific, from simple to complex, very good instructional videos and interesting projects
Remarkably lucid exposition of complex learning architectures with directly applicable programming exercises. Highest recommendation. Thanks to the deep learning.ai staff for putting this entire specialization together and sharing their abundantly clear mastery of the subjects.
by Tun C•
Some of the lectures were not quite up to par with professor Ng's standard. Some of the programming assignments were hard to follow and missing some details. Nevertheless, I came away with good understanding of sequence models and RNN. I can't thank enough. 5 stars from me.
by Julia C•
This is very logical and especially the addition of probability between the words to improve predictions. It will be interesting to compare language , which language is easier to predict and why and study backward- how human creates them. We might learn something unexpected.
by Ayush G•
The lectures were super informative. It's almost unreal how easily he explains such difficult concepts, such they look a child's play. The coding assignments are incredibly informative and super interesting. I am very thankful for this specialization that it taught me so much