Sequence Models

His ability to break down a complicated concept like neural networks and deep learning so that a non-math, non computer person like me actually understands the underlying mechanisms is quite extraordinary.

Very good introduction to RNN, GRU and LTSM neural networks with good practical examples.

Simply fantastic :D The course is well-structured, and a nice introduction to sequence-related neural networks.

Outstanding Very well laid out with good coverage and good insights about what types/sizes of neural networks are prevalent in commercial systems.

Pretty good, for learning recurrent neural network and neural machine translation, and I learned a lot about Keras in the program assignments.

Homework is also very helpful to understand what is going on step by step under Recurrent Neural Network.

This course has helped me understand the basics of Recurrent Neural Network.

We learn Recurrent Neural Network (RNN) /Sequence Model, which allow translation or trigger word (like "Hey Siri!").

These lectures are good for intuition and background of different types of Neural Network architectures.

This is an amazing course, it Provides a great Help...i have learned lots n lots of stuff about NLP, Learn about recurrent neural networks that work extremely well on temporal data, word vector representations and embedding layers --that are explained in a concise manner, and more importantly I love the Attention mechanism, the model that understand where it should focus...... its attention given a sequence of inputs.... amazing amazing ..highly Recommended.... Thankyou Good Thx Andrew et al.

gets your foot into the door of recurrent neural networks!

This specialization helped me overall to gain a solid fundamentals and strong intuition about building blocks of Neural Networks.

Great design of the structure to cover both language modeling and speech recognition.

Very Useful course Gives the student an excellent perspective on how to go about word embeddings & speech recognition.

And I have learned many intuitions and skills of Sequence Models and its application in various NLP tasks and speech recognition.

May be extend this to 4 to 5 weeks and spend little more time on speech recognition, music generation and other audio data processing would have helped.Unlike all other earlier modules, this one had many issues with grader and many errors in note book templates.

NLP and speech recognition intition with real world coding examples.

A very good intro to NLP/Speech Recognition with Deep Learning (RNN) This is the best course I've learnt.

It is better to provide more contents on Speech Recognition.

This Course was a nice introduction to Sequence-To-Sequence Models, where we learned about applications involving speech recognition, machine translation, & captioning of images or videos.

Smooth and hands-on walkthrough of basics of NLP and speech recognition.

It is a very complete course I learned a lot about various network architectures to use in language model, speech recognition, music generation, etc.

Best specializatin course I have found and the insights given in this course are truely wonderfull A great introduction to Recurrent Neural Network models with lots of examples (text generation, music generation, sentiment analysis, word embedding, speech recognition, attention-based machine translations etc.).

very good Amazing project on detecting words in speech recognition excellent!

"Machine Learning" by Andrew Ng is my 2nd Coursera course in 2013, and "Sequence Models - Deep Learning Specialization" is my latest Coursera course in 2018.

Great course and teaching as usual Thanks Professor Andrew Ng and team for the deep learning specialization.

This course, being relatively new, was less polished compared to the other courses in Deep Learning Specialization.

The practical exercises are interesting but I found them in a bit raw state compared to the previous courses of the Deep Learning Specialization.

The best course in the Deep Learning Specialization.

The instructions were really unclear, it could have been better Thank you so much for the entire deep learning specialization.

Muchas Gracias... !The course is very good, particularly I am very grateful to COURSERA, for giving me the opportunity to do the five courses of the Deep Learning Specialization with financial aid and allowing me to have access to this type of training and certification.

As with all the 5 courses in the Deep Learning Specialization, the video lectures were amazing, thoughtfully designed (and separated) and gave an understandable overview of the content.

I think It would be better If there was a capstone project for the final course of deep learning specialization.

This is Course 5 of the Deep Learning Specialization, and the last one.

Wonderful end to this Deep Learning Specialization.

Amazing course Great ending to the Deep Learning Specialization.

I realize though that deep learning requires a lot of practice and experimentation and completing this course (and specialization) is just a tiny first step .. Learnt many useful techniques and knowledge of Sequence Models and Recurrent Neural Networks.

Very pleasant introduction to the field of recurrent neural networks!

Likely due to the rush of getting this final course out Best course you can find about recurrent neural networks.

I learned many new things about practical applications of recurrent neural networks in this class and found the natural language emphasis to be very useful, particularly for certain problems I have been working on for some time!

Professor Ng's lectures provided intuitive ways to understand the complex recurrent neural networks and how to apply it in real world applications.

The course is very interesting and it gives an insight into recurrent neural networks (RNN).

I got to learn a lot very efficiently about Recurrent Neural Networks!!!

This Is very helpful course in order to learn Recurrent Neural Networks.

With this course it's possible to conclude that recurrent neural networks are the most powerful variant of neural networks.

In this last module of the specialization, you will learn in details how the recurrent neural networks works.

Again, nice videos and explanations, and well-designed useful programming assignment (TensorFlow/Keras and numpy) The best online course I've seen anywhere about recurrent neural networks!

Really enjoyed the insightful content of this course Nice intro in recurrent neural networks.I'd prefer more focus on why the architectures are designed the way they are.

Very nice course if you want to apply natural language processing start through python.Very nice course to start your crazy projects in NLP.

I highly recommend this course to anyone interested in natural language processing or speech recognition.

Excellent course regarding Recurrent Neural Networks and Natural Language processing.

Thank you Andrew This course is perfect to learn deep insights of natural language processing, word2vec, speech recognition, trigger word detection and sentiment analysis among others.

Very usefull and structured tutorial The course should contain more explanation about natural language processing like tf-idf,lemmatization,stemming,dialog flow.

But I learned some basic knowledge about Natural Language Processing and Speech Recognition through this course.

Before starting the course, I wanted to have a strong knowledge of the basics of Natural Language Processing as I wanted to specialize in this domain.

This course teaches in-depth knowledge of sequence models in natural language processing and speech regocnition .

Very effective at improving my understanding of RNNs (and its variants), Natural Language Processing, and some basics regarding working with audio data.

Amazing Course to learn RNN and Natural Language Processing.

Another outstanding course about Deep Learning.It teaches Recurrent Neural Networks from the basics up to industry applications such as Speech Recognition and Natural Language Processing.

A great course for everyone who wants to use deep learning for natural language processing.

Would highly recommend this to anyone willing to learn NLP, Sequence Modelling, Word Embeddings, Machine Translation and related stuff.

Loved the Sequence model chapters especially with word embeddings, sentiment classification and Speech recognition exercises Excellent course Great course on Sequence Models by Andrew Ng.

Also I hope the Word2Vec algorithm and word embedding in general is explained better and with exact steps.

I was a bit disappointed about NLP section as it brushed over word embedding and left me without much understanding on how they are estimated.

Word embeddings cleared up a lot, but the entire course was a lot of information to digest at once.

Really good choice of topics, including state of the art tools like attention and word embeddings.

This course offers a great introduction to the models: RNN, GRU and LSTM.In addition, it illustrates the power of "Word Embedding" and "Attention Model".

This course is great to get intuitive understanding of Word Embeddings, RNNs, LSTMs, GRUs and Attention Models.You will have great explainer videos and some excellent programming exercises.

What impresses me most is the lesson of "Debiasing word embeddings", it shows that AI could be designed to do more against human stale thoughts, which sets up a good principle for designing AI.

You can tell there is a lot of work behind it.THANKS Assignments are not up t the mark.. Expected to have high vocabulary size word embedding assignment, Machine Translation assignments Overall it was pretty informational on introducing NLP to me.

At the end you'd learn so much that by just looking at a single slide of an overview of Trigger Word Detection you could make the entire DL model yourself.

I really enjoyed and had fun with the programing assignments specially the Emojify and the trigger word detection.

The programming assignment on trigger word detection gives an insight into the practical machine learning implementation for speech recognition.

I liked the "Trigger word detection" (the last assignment of this course) very much.

Un véritable apprentissage et un plaisir surtout sur le Trigger Word Detection qui était très intéressant.Totally more difficult than anything before.

It was a pleasure, especially for the Trigger Word Detection which was very interesting.

Everything is great except the last assignment - trigger word detection is hard to save and submit, and i can't even open it sometime.

Especially the trigger word detection algorithm worked perfect with my own voice, that was satisfying of course.

Week 3: quite a complected network was used for trigger word detection; however, it is not clear why exactly this architecture was used; specific order of dropout, batchnorm and GRU seems to be a pure magic; at least, a few words why this combination is picked are needed.

Highly recommended to all who what to learn the "deep" in Deep Learning!

Highly recommended.

Again overall - highly recommended The lecture for Sequence model was good motivation to get interesting about text and audio processing.

I really thank you for providing the amazing course highly recommended for everyone, it is difficult at the beginning but at hte end of the course will be understood It' a excelent course.

Highly recommended Very Useful Highly recommended course to understand the concept of speech recognition models, very real use cases.

Highly recommended!

Highly recommended course I learned a lot, thank you very much!

This is highly recommended for the deep learning and machine learning enthusiasts.

I am looking forward to more courses/specialization from DeepLearning.AI team in the coming months :) Some programming assignments have few errors in them (wrong equations, wrong expected output etc.)

Looking forward to any course Andrew Ng might teach in the future.

I'm looking forward to have a next level course on top of this track.

Great course content and well explained by Andrew, looking forward to apply the learning to solve real world problems.

Kinda sad that this journey is over but looking forward to applying all that I have learnt.

I have gained so much confidence after completing these set of 5 courses and looking forward to build some cool projects on my own using the concepts that i have learned in past 5 months.

Looking forward to new courses - maybe Reinforcement Learning, GANs?

Overall, I do highly recommend this course, but be forewarned about the need to be familiar with Keras before starting.

However, I'm not very familiar with Keras and working on the Keras code really takes my time even I'm quite experienced with Tensorflow.

Before this course, I spent many hours reading papers on LSTMs and trying to figure out what is going on with all these "Gates", but couldn't understand intuitions behind them.

It is particularly frustrating for those trying to work on the optional/ungraded programming assignment sections that have some incorrect comparison values, as much time will be wasted trying to figure out the source of the error.

I feel that first 3 courses were much better prepared; though CNNs/RNNs are still super-useful despite not being 100% polished.

While the first 3 courses raise your knowledge of ANN in preparation to the 4th one, it is a little more difficult to understand this 5th course.