Oct 31, 2017
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.
Dec 24, 2017
Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.
by Rajeev B•
Nov 18, 2017
The assignments are very good. All the parameter update methods are explained in a very good manner. I would recommend it very strongly for anyone who is looking for an in depth understanding of why we do what we do for tuning, regularization, optimization of NN. All the implementation in the assignments is also from scratch, so, that really helps a lot. I felt this is better than Stanford CS231n course material, after all this is a whole course on this specific purpose :).
by Marcel M•
Jun 01, 2018
This course a practical way of fine tuning your model in order to improve on its performance. Rather than Deep Learning being a "so-called" black box. It turns out that Machine Learning models are not black boxes but rather there are proven techniques of not only finding out what happens in them but also to fine tune them in a systematic manner in order to improve on their results. It is an excellent course for the practical Deep Learning Engineer. Good Job and Keep It Up!
by Artem M•
Apr 22, 2018
Found a lot of interesting details about NN that I did not know. This is a much better course than the first one. IncludesTensorflow exercises, which is useful. Nevertheless, proofs are still omitted for some results like initializations. It is not hard to google, but I bet lecturers could explain them much faster than diving into science literature. Otherwise, intuitional explanations of Adam using exponential smoothing, or physics analogy of momentum are just brilliant.
by Daniel C•
Jan 14, 2018
True to the claimed learning objectives, the course Improving Deep Neural Networks shows some of the magic behind deep learning algorithms. The programming assignments solidify abstract concepts discussed in lecture videos. In fact, some portions like seeing cost decreases in real-time for Adam Optimization are truly eye-opening experiences.
One possible improvement is better editing of instructions and code comments of TensorFlow Tutorial Programming Assignment in Week 3.
by Pedro B M•
Feb 04, 2019
As always Andrew Ng is very didactic explaining different and complex hyperparameter tuning techniques and optimizations algorithms, giving intuitive explanations and examples. I've been learning a lot in these courses! And more than that, the content is presented in such a way that motivates the student to go beyond and explore/try different implementations and problems to apply. I highly recommend the course for anyone who wants to become a serious ML practitioner!
by Johnathan T•
Sep 01, 2017
This class was awesome! Thank you to Andew Ng and his team for putting this Specialization together. It is amazing for someone with so much experience in this field to be willing to share their wisdom with everyone, practically for free. The course content is filled with information that would have taken me years of to acquire. I am fortunate to have the opportunity to build a strong foundation in this field at a time when A.I. is becoming society's new electricity!
by Anton V•
Jun 13, 2018
A very valuable course to improve your understanding and develop a better toolset in using NNs. The instructor gives great tips on how to approach problems and explains the latest techniques very well. Also features a nice introduction to TensorFlow. As an experienced programmer I found this course to be a breezy and fast hands-on tutorial to get you going quickly if you are doing these courses to apply for something you are interested in (e.g. personal project)
by AVADH P•
Jan 07, 2020
Excellent course!! Really glad to have taken this course as a part of the Deep Learning specialization. This course gives a breakthrough in designing neural networks and deep networks using a thorough understanding of all the major aspects to be considered. The course also helps in learning current industry-wide used opensource frameworks such as TensorFlow. The assignments are well designed to make the step by step understanding and exercise of the learning.
by Matheus H B d A•
Sep 22, 2017
Um dos cursos que mais gostei até o momento. Desde que comecei a estudar deep learning vejo se falar de muitas técnicas que pareciam impossíveis de compreender e implementar, mas esse curso não só ensina como implementar algumas delas, como também ajuda a entender o motivo dessas técnicas serem tão boas para os modelos de redes neurais, dando uma boa intuição de como cada método funciona. Além disso, apresenta e ajuda a desmistificar o framework tensorflow.
by Joe M•
Jul 14, 2019
This course was a great continuation of the first. The lecture pace is great (and ability to speed up or slow down the video speed helps a lot), the reiteration of past lessons helps with some of the denser materials, and the overall presentation is excellent. Also very nice that the problem sets aren't out to trick you! The material is new enough to many of us to begin with! The emphasis on practical application of the material is key (for me, at least).
by Ricardo S•
Dec 17, 2017
The course covers an extremely important topic (I know I've been lost in hyperparameter maze before) , and allowed me to get a good feeling of what, when and how to use hyperparameters. I guess that to actually master the topic students will have to practice with their own models and data sets, therefore I think that getting actual practice on this topic would be out of the scope of the course, and thus I think the programming assignments were adequate.
by Holger O•
May 23, 2019
Prof. Andrew did it again! I took the "classical" Machine Learning course and I'm pleased to see that this continuation was as good or even better. A total equilibrium between the mathematical depth you need to understand the basis of the algorithms and the practical skills you need to put them in practice in the real world, in the exact amount for them to fit in a 18-hour course. As a starting point, this course is perfect! Eager to keep on learning...
by David F•
Sep 16, 2017
These courses are awesome. Andrew Ng is a very clear professor and the interviews with other ML practitioners are enlightening. My one criticism is that the assignments are put on a plate for you so they're pretty easy to complete but then difficult to replicate in real life (since so much of the scaffolding was taken care of for you while learning). But maybe that helps to preserve the flow of the class, rather than getting you bogged down in details.
by Sergio B S•
Aug 01, 2018
I began using Deep Learning Frameworks before this course, but...
I realise now, after this second course and the first one, that learning the maths behind Neural Networks helps exponentially to understand and internalize what is the real use of some of the most important hyperparameters and the what's and why's of good strategies to regularize models. As A.Ng repeat sometimes, this specialization help me "To get the intuition" to improve the models.
by Amit K•
Dec 04, 2018
This is good course for the student, who want to do real stuff with NN. Some of the tricks are well explained like L2,dropout, adam, momentum, minibatches etc. I think these are much needed tricks if i need to implement and tune my own NN on my own problems. I prefer to have a second level of such course which really talks about challenges in real life NN and how to solve those. Once again thanks alot for the entire Team for pulling this together.
by Eleanna S•
Mar 04, 2018
Very useful course. Gives great insight on the hyper parameter tuning, regularisation and optimisation. One request I have is to provide a docker image which we can use to run the exercises locally. Sometimes I found it hard to build the environment where I can run the coursework. Some of the installations are clashing and it is not clear what versions of libraries are used in the coursework environment. It sometimes requires unnecessary effort.
by Hugo v d B•
Sep 26, 2017
In the second course of the Deep Learning specialization Andrew gets deeper into the different subjects of Neural Networks. Again he does a great job in explaining both the math and the way you can improve the outcoming of deep neural networks. The quizzes and assignments where helpful and not difficult at all. He also shows some good frameworks to work with and gives a nice introduction to Tensorflow. I'm looking forward to start with course 3.
by Parab N S•
Aug 25, 2019
Excellent course demonstrating the ways to improve the accuracy of the deep neural networks. It had been the case with me that I could create an initial model easily, but getting an expected level of accuracy was difficult. This course has made it much easier for me to improve th performance of my deep learning models within a short span of time. I would like to thank Professor Andrew N.G. and his team for developing such a wonderful course.
by Xizewen H•
Oct 06, 2017
This course is where the specialization really distinguish itself from Udacity's deep learning nano degree program -- the model fine-tuning part is very important and there are lots of details can be talked about, but Udacity somehow avoided going into details for it. After taking the Udacity's course first, I feel this course really helped me refreshed some knowledge I learnt as well as teach me much more. Definitely recommend this course!
by Ivanovitch S•
Feb 29, 2020
This course is a bit more hard than the first one. I recommend using paper & pencil in order to reproduce all the equations. I gave five stars because the all material is very well described, however, the last part of week 3 must be improved, mainly that related to the practice assignment. There is no link between the Batch Norm and hyperparameter tuning with to practice assignment. Additionally, TensorFlow 2.0 should be introduced too.
by Ayush K J•
Feb 10, 2018
I will recommend this course to beginners in deep learning. As this course has helped me learn about following topics.
Bias/Variance tradeoff, Different types of regularization methods, Code optimization techniques to speed up learning weights, Different types of weight optimization algorithms , About Hyper parameter tuning, Method for normalizing activation as batch norm, About Multi class classification and An introduction to Tensorflow
by Marcio R•
Nov 15, 2018
Excellent course overall. The explanations given are very intuitive even for complex concepts. The teacher always made sure to ease out any concern that might appear if the topic being discussed is not fully grasped yet. I believe that this is a very important step given that MOOC courses should be open for every one, every person has a different learning rate. I highly recommend this for anyone looking to delve deeper into NN and DL.
by Arun S•
Apr 13, 2019
This course helped me to understand the practical aspect of NN. Tuning of Hyper parameters, Regularization , Algos like ADAM are important for fast and accurate training. I hope i could make use of information in future. However this course gives very little introduction to tensorflow and somewhat doesn't satisfies students i believe. Prof. Andrew Ng gives a fantastic lectures covering all important aspects in details with patience.
by Teyim M P•
Jan 01, 2018
Though the course was mostly theoretical in content, I believe it taught some of the most important concepts in any machine learning undertaking - making the system achieve higher accuracies. Although I found the course content too compact and things kinda move really fast, I think going through the videos a second time even at a 2x speed would make it all stick. In all, it was a tremendous course. I love Andrew Ng's teaching style.
by Yonas T•
Oct 28, 2017
An excellent class and loved the tensor flow tutorial. One thing I would also like to mention is the fact that Andrew made us do the algorithm coding in the first class from scratch helps a lot to really understand the basics of the neural networks. When you then move to using tensor flow it gets even better. Thanks for whole team, Andrew and all the students around the world who makes the environment/forum so vibrant and helpful.