Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.
After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.
By K W•
Andrew Ng's Deep Learning course is phenomenal. He patiently and cleverly focuses on explaining the intuition behind concepts like regularization, normalization and optimizers in bite sized chunks, rather than drowning students in linear algebra. The guest speakers are giants in Deep Learning, literally the guys who wrote the textbooks. It's like a religious summit where the guest speakers invited are Jesus, Allah and Budda.
By Sikang B•
Clear and practical, this course sets a good bridge from the old NP based programming model to the modern programming models of using Tensorflow and Keras. The optimization methodologies lead to the very useful aspect of ML: hyper-parameters tuning. Though a lot of these hyper-parameters still feel magical, it is super helpful to know more about them.
Suggest to clearly mark this course as a requirement for course 4 and 5.
By Chinmay K K C•
I finally got to delve deeper into the intuitions behind the choice of hyperparameters and optimisation algorithms. It is incredible to see how even the smallest of choices can affect our model's performance and understanding the effects of certain choices of hyperparameters on the overall performance of our model will help us make better decisions in regard to how we set up our models. This course was totally worth it!
This course builds upon the fundamentals learnt in the first course. By doing this course I have learnt the importance of regularization, and initialization of weights while training a neural network. The course also gives information on implementing neural networks on large datasets and how to methodically choose the hyperparameters. The course exercises are informative and helped me in solidifying the theory learnt.
By David B•
Excellent course - my only complaint is that the grader is really finicky about completing the notebooks in a very specific way. Your submissions get rejected in a very cryptic way if you use certain valid TensorFlow constructions, namely you cannot use "Z = W @ X + b", instead you must type "Z = tf.add(tf.matmul(W, X), b)", which I find much more difficult to read. Nonetheless, I think this was an excellent course.
By Heinz D•
Great course, great instructor and staff. Good speed and good hands-on exercises. Some flaws in the downloadable material and a couple of everlasting corrigenda, but nothing too serious. Integrity control could be enhanced in the TensowFlow assignment. I wish there were not only quizzes at the ends of the weeks but also inbetween or even within the lectures. Looking forward to the next course in this specialization.
By Jaime M•
Very good course as well, although the exercises need some "debugging" there are some typos and errors. I found that the previous courses exercises where too guided, too easy in some points. In this case are more tricky, but not in the correct sense. I would orient a bit more the way of thinking or refer to external sources to get a bit more on track with TF before coding. Nonetheless, all in all, is a great course.
By Renzo B•
It was a very insightful course. I learned the basic intuition behind the concepts that Andrew Ng explained. For my suggestions, maybe the deeper derivations and meanings behind the concepts could be discussed in video or just a reading material. For example with the maths behind regularization, batch normalization and etc. could be discussed more in depth in a reading material. All in all the course was excellent.
By Mehedi H•
Very good one. It was great pleasure to learn momentum , RMSProp and then coming to know how to combine them in Adam. Tensorflow example was great. In tensorflow exercise, using regularization can give a boost in the generalization of data which has been mentioned (and I tested it )-but this could have been a part of the exercise.
However, starting to audit the next course of this series. Best of Luck for me !! :D
By Mikhail G•
Very nice course, worth taking for everyone who is interested in ML/Deep learning, including the very beginners and professionals. I work at the edge of Neuroscience/ML/AI, I have a strong theoretical ML background, but little practice. Even though I was familiar with many of the concepts before taking the course, it was still extremely useful to hear about it again and have way better understanding of the topic
By Chong O K•
The course covers many regularization and optimization techniques of deep neural network. The instructor can explain the concept and theory of those techniques using easy-to-understand analogies and example. He also used visualisations like diagrams and charts to make the explanations intuitive. The assignments are very comprehensive and mimic real-world examples that let students build a very solid foundation.
By Ganesh S V M K•
First of all, I would like to thank Coursera for providing the course. I would always be in debt to Coursera for providing me with financial aid. This website is one of the best online learning platforms. Love the way the assignments are provided. Even I have a bit of understanding and experience in deep learning, this course clears all the blue skies in between and makes deep learning looks simple to learn :)
By Lyle T•
Very good in-depth coverage of mini-batch, ReLU, Adam, L2 and dropout regularization. Good overview of batch normalization. Brief but useful intro to Tensor Flow (including programming assignment). In general, the programming assignments are pretty easy, but a bit hard to debug in the Jupiter notebooks, though I was able to get things working by inspecting the code to locate typos.
Summary: Highly recommended
By Jonathan M•
Builds upon the concepts that were explained in the first course in specialization and Andrew Ng's Machine Learning MOOC and really goes more into depth about regularization and optimization techniques. The introduction to frameworks at the end of the course does a great job of showing how this can apply to other concepts. The programming exercises and course material are great overall and very informative.
By Jingyu Z•
This Course is really good for the beginner of NN and deep learning. It tells me what to consider and how to consider for model build-up. I also like the quiz which helps me to check my concepts understanding, the coding practice is easy to understand and I can logically learn how to practice my understanding of this session. I also love the interview session with DL Heroes. This course is really inspiring.
By Sakshar C•
This course really helped me to get a proper hold on how to work with hyperparameter tuning in an organized and efficient way. I used to think of it as a "voodoo" magic, the way one can fall upon the exact set of values for hyperparameters. Now, I think that I have a better concrete idea of how to approach tuning for improving a neural network according to the available resources and also the applications.
By Marlon A C V•
This course is AWESOME, a lot of new things related to Deep neural networks regularization techniques, initialization techniques and Tensorflow Neural Networks modeling. A step forward into mastering applied Artificial Neural Networks!! Course really recommended for ML/AI enthusiasts and begginer or promising researchers in the field. I recommend to take all the courses provided in this DL Specialization!!
By Kyle W•
Great course. I'm particularly happy that they chose to teach TensorFlow. There were a number of typos/errata, which is to be expected with such a new course, but it looks like they are working quickly to address them. Overall, I feel more confident implementing neural nets than I did after the original ML course taught by Andrew Ng.
Watching Andrew try to draw a horse in one a the lectures is a huge bonus.
By Rohit K•
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.
By Itsido C A•
This is a must to really understand and master the art of machine learning. With this course I understood that building a model and training it is not even half of the story of being a machine learning engineer, without knowledge of how to tune the models parameters you might not be able to deliver product on schedule. Thanks for Dr Andrew and the team for an awesome content and learning experience.
It is really a EXTREMELY GOOD course for a bad-basic student, according this course, not only I have know the theories, but also the pratical project.I do think now I know the BN, the Hyperparameter, and the Regularization and so on in Deep Learning field! It would be very helpful for me to step into the AI!
and both videos and lectures are very important for new comers in deep learning ! THANKS ALOT!
By Nouroz R A•
This is one of the best MOOC I have ever come up to. Very informative, well explained and easily put. This course helped me to learn so many new things that I had missed in books and research papers. Thanks Andrew Ng, this was like a debt to me. As a wannabe deep learning researcher/Engineer, your contribution to help me catch the basic concepts will always be remembered. :-)
Yes, highly recommended.
By ali m•
It was a joyful experience, I've learned some amazing new ideas like exponentially weighted averages and Adam optimizer. I think Dr. Andrew is an amazing teacher, he teaches us some of his experience in the field so we could explore his way of thinking and learn too much from him. After all this course is very helpful to everyone starting a new journey in The Deep learning world so THANKS A LOT.
This course has really helped me alot in gaining better insights about improving deep neural networks by tuning the required hyperparameters. It has also increased my understanding of the previous course and I would definitely recommend this course. I would like to express my gratitude from the bottom of my heart to the Coursera team and the specialization course team for such an amazing course.
By XiaoLong L•
After reading the Deep Learning book wrote by Ian Goodfellow, it's much more easy for me to complete this course within two days. I've gotten a lot through this course and know more detail about the deep learning hyperparameter tuning, regularization and optimization methods now. Thanks so much for Prof. Andrew and TAs. I will keep learning the 3rd course in this specification of deep learning.