Nov 23, 2017
I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
Mar 19, 2019
Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.
创建者 Bhavul G•
Apr 22, 2018
I feel humbled as I ended this course, realising that years and years of knowledge that Prof. Andrew and others have gathered they've just let out to public, accessible to everyone. It is such a great act of kindness. I am really thankful to you folks. This was a great course to learn the insights of an experienced ML / DL guy. It would help a lot when I'll actually be working on a real life project. I hope I would be able to spread the light of knowledge even further.
创建者 GurArpan S D•
Oct 21, 2017
This may be an optional course in the deep learning specialization, but I beg to differ. If you plan to do actually start a project in machine learning, it is imperative you take this course. You could finish your project ten times faster with a fraction of the work. All in all, every one of these courses in this specialization have been beautifully organized and taught very well.
Thank you so much for offering this course along with the others in the specialization!
创建者 MANRAJ S C•
Oct 27, 2019
The course is really great! It offers an in-depth understanding of the practical aspects and applications where deep learning can be applied. Most importantly the content in this course will help you iterate faster with your machine learning problem by doing error analysis on it. This course tells you exactly what can be done in which situation to improve the performance by analyzing the data and other statistical aspects of the data as well as the algorithm.
创建者 SK A F•
Jan 10, 2020
Error analysis and Learning the methodology to handle the errors. Besides the traditional systematic way of performance analysis like train-dev-test and cross-validation, Andrew focused on data mismatch and train-dev data. These two are the most important things that are described very well. Like other courses, Andrew was very good to describe the real-life practice. In this course, two simulation quiz really helps a lot to deeply understand the application.
创建者 Ryan M•
Sep 14, 2017
This is a VERY valuable course, with tons of practical advice on how to understand problems all machine learning / neural network developers experience and how to tackle them. I have never seen such high quality practical advice in any textbook or in any other course before, and I believe that even those who are not taking the full five course deep learning specialization should seriously considered this course. Another truly excellent Andrew Ng course!
May 01, 2018
This course introduces some general principles for developing a deep learning project. It points out the difference of setting of train/dev/test sets between deep learning and traditional machine learning. That's a practical advice. And it's notable to include human performance and regard it as Bayesian bound, almost the best we would expect an algorithm to achieve. That saves you from spending unnecessary time to make a subtle improvement. Learnt a lot!
创建者 Stanley C D•
Sep 08, 2018
In this course I learned about ways to approach some of the real world challenges that I have already faced on some of my own projects. For example, what actions should you consider when you find a significant number of labeling errors in the dev/test sets that affects your ROC. I also was motivated by the last module on end to end training and the interview with Ruslan Salakhutdinov to pursue an end to end training idea that I have been thinking about.
Nov 02, 2018
Great Course! Many students will choose to skip this course since they think there are less knowledges than other course in the 5-course specialization. But I have to tell you: this is the best course in the specialization, because you can learn a lot knowledges especially skills and experiences in practice from this course that you can't learn from other books, courses or universities. BTW, I'm not telling that the other 4 courses are not important.
创建者 Daniel S•
Dec 17, 2017
Andrew Ng is brilliant! I have never seen such a great tutor in my life. He bring extremely useful concepts and explains them so easily in a way the concepts stay in your mind.
Like the backprop algorithms he talks, he has learned so much from his old course and he has made great improvements to focus on New people. He sure has a good deep network up his brain that has gone through lot of iterations (without overfitting) with beautiful set of features.
创建者 Kryštof C•
Nov 07, 2018
It is very good probe to practice. I would very appreciate to take this course before I have started in machine learning. It would help me to realize some mistakes I have maid before. On the other hand, for people, who have some experience with machine learning, some chapters are being over-explained, as the topics are quite clear to those people. Overall: I would recommend this course to everyone, who wants to start with his/her own NN training.
创建者 Teguh H•
Nov 29, 2017
No coding at all. But this is one of the best course on AI, because it does not talk about coding or anything, but most importantly, the one thing that is not taught by many others. Experience of Andrew Ng trials and errors in approaching ML projects. How to create structure, how to observe what results to see. In short this course is like 'how to save time in doing AI projects and make optimal use of it, avoid trial error which can cost months.'
创建者 Luis C G•
Oct 19, 2017
Despite of its relative simplicity (from a technical point of view), it is probably one of the most practical courses I have taken in Coursera. Even though it only mentions deep learning, the overall methodology can be applied to any machine learning work. It is important to get familiar with the heart of the models, but it is probably even more important how to work on an end-to-end machine learning project. In summary: Highly recommendable!
创建者 Danilo Đ•
Jan 04, 2018
Unlike most of the Deep Learning knowledge which can be found in literature and other MOOCs, this course provides you with insights that can only be acquired trough (often painful) trial and error. Here you learn how to approach Deep Learning projects, how to avoid most common mistakes, and how to quickly identify errors in your model.
Do yourselves a favor, and finish this course before taking on your very first DL project.
创建者 Johnathan T•
Sep 04, 2017
This course is my favorite so far. It has really given me a way to systematically and strategically set up a machine learning experiment and iterate in a way that make sense. For me the toughest part of ML projects has always been figuring out where and how to start. Now that I have some solid guidelines to follow, I don't feel as anxious about jumping into a new problem and it turning into a wild goose chase. Thanks a lot!
创建者 Shankar G•
Jul 03, 2018
Wow! This course was more of real time application scenarios and the kind of tweeks to build, transform learning plus multi-task learning was excellent. The end-to-end learning with a split approach of solving was really something new I found in this learning. Not to forget the application level quizzes were so tricky it was challenging to understand and interpret the possible solutions but, was great learning experience.
创建者 Robert K•
Nov 19, 2017
Fantastic lectures combined with case-studies for real world applications. In this course you don't program, but don't underestimate the ability to abstract out and systematically assess your thinking. This could speed up your project development and save you tone of time. Any potential employers would also be happy that you know some practical aspects of implementing a deep neural network for a particular use.
创建者 Shringar K•
Jul 28, 2019
Honestly speaking, this is the best course in the whole deep learning specialization. This course is the one which tells us what to do as a Deep learning engineer in real world scenario.
People can do the coding and everything, but without proper directions the product might fail.
Andrew Sir has given his expertise in a very neat and compact way, good enough for starting our own research or whatever we want to.
创建者 George Z•
Aug 04, 2019
Amazing 3rd course, I learned so much related to error analysis, bias, variance, data mismatch, data synthesis as well as transfer learning, multi-task learning, end to end deep learning and more. I really loved both case studies in the end of each week. The interviews especially with Andrej Karpathy was my favorite :) Excellent best practices and strategies that you don't learn from any other course or book.
创建者 Johan B•
Sep 22, 2017
This course in the specialization is less about how to build a model but gives you a structured way of how to approach a deeplearning project. It shows how much some manual (and maybe boring) counting can speed up your project and that starting with a simple model and iterating on that often outperformes very detailed thinking about your project at forehand.
The practical tips Andrew shares are very valuable!
创建者 Sebastian E G•
Aug 18, 2017
Liked this way more than I thought I would. Machine learning project management is vital in a professional setting (I would assume), and I often leave it as an afterthought. It's not just building the fanciest model, it's about how to iterate from an okay model to your best model in an efficient manner. This course teaches you what to look for with your results and pinpoints what areas to focus on to improve.
创建者 Johannes A B•
Feb 27, 2018
Very nice introduction to the aspects of a machine learning project that is not covered other places, but is very important. Most of it is very intuitive and comes as no surprise, but it is still very usefull to collect it into a single course. It is a good resource to have in case you are in doubt about how to structure your project, where to focus your energies and how to make progress in a systematic way.
创建者 Kyle H•
Jan 04, 2018
Great course by Andrew Ng, coming from his Machine Learning Course and seeking to work on Kaggle Competitions, this course provides all the knowledge necessary to approach any machine learning problem (with or without a team), and efficiently work towards a better algorithm. It's almost as if he gives you the tools necessary to optimize yourself which in turn allows you to efficiently optimize any algorithm.
创建者 Rohit K•
Jul 06, 2019
Hello Andrew, I am a big fan of you. Learning from your every course. Very unfortunate that I can do that remotely only.
One thing that I want to mention - Can we have lecture notes on coursera, just like the way used to in CS229 that we can read before coming to next lecture. I found that that was very useful in understanding when things get harder.
Thanks hope we can improve coursera in that matter.
创建者 Claude C•
Jun 08, 2019
Good engineering tips, tricks, bolts and nuts, very useful! Andrew Ng is more dedicated to engineering and best practices that are very important in the machine learning field which is not only theory (a lot less than some believe or pretend) but very empirical, with a lot of practices, try and and error, recipes. Don't be snob, maths are awesome but good engineering and best practices are crucial too.
创建者 Kishore K T•
Jul 10, 2019
My Sincere "thank you" to Andrew Ng for teaching me ML and Structuring ML Projects. I find the content and presentation are on the highest level; which will definitely make the learner to think and workout in the correct direction when working in ML engineering and/or managing ML projects. I believe, in the coming times I'll learn more relevant topics from him to excel in my career.
Thank you again.