Course material was great, however the use of Tensorflow in the exercises requires more background than provided in the short tutorial.

I get the dynamic aspect of writing the lecture notes in the videos, however the lecture notes should be "cleaned up" in the downloadable files (i.e., typos corrected and typed up). Additionally, the notes written in the video could be written and organized more clearly (e.g., uniform directional flow across the page/screen rather than randomly fit wherever on the page.

I liked that the course gradually introduces more and more complexity and concepts without making your drown. Even though existing frameworks (e.g. tensorflow) can be used so that most of the complexity is hidden it is still required to understand why one method should be used rather than the other. This course I believe addresses this (as well as first in this specialisation). I still wish there was more information on details but probably all that is needed are external links to extra material.

Fantastic course! You can experience short, easy to understand lectures, followed by plenty of opportunities to implement covered material, and most importantly create optimized image classifiers - like cats, dogs. I liked how up until the end of the course you had to implement everything from the scratch, not just using read-made frameworks. Finally, you are introduced into frameworks, but this deep understanding stays with you. 5/5 recommendation. Bye, I gotta finish the rest of specialization.

Good course explaining the concept of hyperparameters vs. parameters, how you can tune the hyperparameters, as well as different regularization techniques. It also provides good explanation for different optimization algorithms (enhancements to stochastic gradient descent). It is a highly recommended course for those who want to understand what is happening under the hood when using a neural network framework, like tensorflow. In the last week a brief introduction to tensorflow is also provided.

The course is great as I expected. It would be helpful if more mathematical background in videos or notes can be attached in each weeks. Moreover, more code could be given to us to write by ourselves to get much better intuition. Rest each of the specializations are awesome as was the first learning Andrew Ng course on Machine Learning. Thank you for providing such courses. This is a great deal for all such students who cannot afford to attend Ivy leagues due to their own reasons.

Great Work!!

It explain neural network from the start. After doing all the 5 courses on deeplearning, it is hard to remember normalization formula, and every details. Sometime some hyperparameter look like a little bullshit: You don't know how to do : add one hyperparameter and go for an argmax. But if it is how its work, then it is okay to learn it; Be ready to laugth and do not compare to pure methods like genetics or Bayesian programming that often work good. But every one should follow this course.

Insights into best practices and directions for common problems make it an one-of-a-kind material for learners. Andrew, as always, has been commendable with his tutor team, the exercises are well cleaned up and in good shape. May be, if some optional tough exercises are given, it will add more value.

The topics of this course, such as the setting of hyperparameters and the use of tensorflow, are critical topics for me, and in this course they are explained both in lectures and in practical tasks.

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.

the only thing i didn't have completely clear is the barch norm, it is so confuse

Very insightful. it would be nice, however, if the course had more information about Tensorflow 2.0.

体验很棒，喜欢这种有作业有评分的课程

very practical.

There are both areas needing improvement and places where this course excels.

To begin let's consider what I think needs improvement.

Since this program says that it does not require prerequisites, it really ought to provide backup reference materials specific to course content. Specifically I found it difficult to follow details without the basics of differential calculus, matrix algebra, Python, and TensorFlow. One alternative is to hire a tutor, which is what I did.

Although there is an active community and tutors, support from the course's owner deeplearning.ai can be improved. For example there are comments in the forums about how long it takes deeplearning.ai to fix bugs in the code.

My last suggestion for improvement is how this course is taught. There is so much content that questioning needs to be more frequent. Currently grading is done through ten multiple choice questions and a programming exercise after a week's worth of videos. While the programming exercises are good, learning would be improved significantly by including three or four questions with each video, even if they are not graded.

Overall, I have the sense that deeplearning.ai has not been improving or updating this course.

Nevertheless this course still deserves four stars. The presenter is well organized, articulate and enthusiastic. The entire course follows a coherent plan. This course and its predecessor supplies a great deal of content. Each video runs 6 - 10 minutes on average which is about the right length, However I was always stopping them to write down points to better grasp the content.

As mentioned, there are a few bugs in the programming exercises. However they are rigorous, cover the material, and effort has been made to make them interesting.

Overall, while there is room for improvement this is still a worthwhile course.

I enrolled in this course without taking the previous ones (I have already done an MSc in Computer Vision and Machine Learning so I thought I wouldn't need the others), but the material has been easy to follow and understand. It is really interesting as it helps you understand important concepts such as bias and variance, or why does batch normalisation work. Sometimes Deep Learning can be seen more as an art than a science, and this course is helpful for defining a good strategy when carrying out deep learing experiments.

The course began from very basics to complex functions, hyperparameter tuning is efficient in building better models, Kudos to Sir Andrew NG for explaining all of them in the simplest way possible. I would highly recommend this course to all interested in deep learning. But I believe that assignments can be made more challenging rather than just filling up the codes with syntaxes. Logic building is very important.

Add more programming assignments to clear fundamentals.

The video material in this class is excellent, as usual. However, I feel that the coding assignments in this series are vastly inadequate. I already felt that there was a lot of "hand holding" in the previous course, but the Tensorflow introduction in this class unfortunately takes things to another level. The assignment, which could be a very valuable exercise to get people acquainted with Tensorflow, amounts to copy and pasting a few lines of code that did not teach me the skills required to approach a problem other than the specific image recognition example given.

I did not think this was a great course, especially since it's paid. The programming assignment notebooks are very buggy and the course mentors are of varying quality. It feels more than a bit unfinished. It also covers two completely different topics - tools for improving deep learning nets and tensorflow - and doesn't make much of an effort to integrate them at all. The course could have used at least one more week of content and assignments to better explain the point of tf.

I can't open week1 assignment initialization, and can't get any help from coursera. I've completed all five courses, but can't get certification without this assignment....

Hello. I am Kunjan Mhaske, a graduate student of Computer Science completing (fingers crossed!) the MS degree in December 2020. Currently, I do not have funds to take university courses till August and hopefully, I could secure the co-op or internship for August to December 2020 so that I can fund my remaining semester from it. I am interested in AI and Data Science field and currently, my major is in AI with Computer vision and Machine Learning. I heard very positive reviews about this Specialization course of Deep Learning from my friends and I wanted to complete it this summer so that I could cash this knowledge in my full-time job or internship hunt. Please refer me for any opportunity I am very much in need of financial support for the completion of MS degree as well as living costs. My email is km1556@rit.edu and https://www.linkedin.com/in/kunjan-mhaske is my Linkedin profile. Fortunately, Coursera offered me the financial aid for the first and second courses in this specialization which turns out to be very helpful for me in this situation. I have applied for the rest of the courses in this specialization. Hopefully, I could get financial aid for all the 5 courses. Although I have a 3.76/4.0 GPA, the depth of concepts explained in this course is very good for my level. I have completed this course in 6 days and I am already feeling confident about the field of Deep Learning. Thank you so much for this wonderful course material and your support. God bless you all.

I had taken Andrew Ng's Machine Learning course. I went on to learn Deep learning from other tutorials and I always wished there was a course on Deep learning too by Andrew Ng. And now that there is, It was worth the wait.

1. All the topics are arranged in logical order. So you feel like a tour of deep learning. Earlier I had to refer to multiple sources for different topics and they usually had different naming and notations which were really confusing.

2. Having taken about 6 top rated courses on AI domain, I can assure you Andrew Ng is the best in his teaching style and content.

3. Exercises and theory go hand in hand. So, you know how to implement as soon as you learn theory.

4. Out of a lot of techniques in each topics like Optimization, Regularization etc. this course picks the most contemporary techniques. This helps you not to wonder which techniques to use in your work.

Overall, This Specialization is like a cookbook for AI. My appreciation and gratitude to Andrew Ng and his team for their contribution to AI.

First of all, I thank Professor Andrew Ng for offering this high quality "Deep Learning" specialization. This specialization helped me overall to gain a solid fundamentals and strong intuition about building blocks of Neural Networks. I'm looking forward to have a next level course on top of this track. Thanks again, Sir!

I strongly recommend this specialization for anyone who wish get their hands dirty and wants to understand what really happens under the hood of Neural networks with some curiosity.

Some of the key factors that differentiate this specialization from other specialization course:

1. Concepts are laid from ground up (i.e you to got to build models using basic numpy/pandas/python and then all the way up using tensorflow and keras etc)

2. Programming Assignments at end of each week on every course.

3. Reference to influential research papers on each topics and guidance provided to study those articles.

4. Motivation talks from few great leaders and scientist from Deep Learning field/community.

Used to tune hyper parameters based on experience... after this course, know more about the internals and from now on, not just know HOW to tune, but WHY it needs to tune this way.

As always, Andrew did fantastic work here to help explain complex formulas in simple and CLEAR way.

Highly recommend it to anyone who fight with overfitting, hyper parameters tuning, etc. It may not help you instantly become a better AI person or help you immediately help you on your day to day programming - as you most likely use various frameworks (Keras/TensorFLow/PyTorch) instead of raw NumPy. But it does help you in the long with better knowledge. It is kinda like show you how the engine works, before teach you more driving skills. It won't help you when your car is working fine, but when it breaks, you know how to troubleshoot and what is the right direction to go. Honestly, I personally think the debugging part is the toughest part of AI.

Take it. Period.

I'm Zeyad, an undergraduate of Computer Engineering at Alexandria University in Egypt.

Taking this course really helped me to learn and study this field and also to implement it. It helped me advance in my knowledge. This course helped me defining Deep Learning field, understanding how Deep Learning could potentially impact our business and industry to write a thought leadership piece regarding use cases and industry potential of Machine Learning.

This specialization helped me identifying which aspects of Deep Learning field seem most important and relevant to us, apparently they were all important to us. Walking away with a strong foundation in where Deep Learning is going, what it does, and how to prepare for it.

Deep Learning specialization helped me achieving a good learning and knowledge about that field.

Thank you so much for offering such wonderful piece of art.

Best Regards,

Zeyad

This is a really informative course and really crucial if you are planning to do a personal project or even prepare for interviews. It equips you with all the tools to get started with actually start implementing Neural Networks for a problem by 1) Teaching how to prepare data sets 2) Regularization/dropout to increase accuracy on test set, 3) Set up your optimization problem 4) Teach different Optimization Algos 5) Teach Hyperparameter tuning and the order of importance of different hyperparameters 6) batch Normalization and lastly Tensorflow. Andrew NG is truly a master in teaching concepts in an approachable and intuitive way. I believe the course can be made even better by adding Keras to the programming frameworks module along with more videos and programming exercises for data pre processing.