This course is very well designed and the instructor does an amazing job at explaining the concepts making it easy to learn, even for a novice in the field. This course helped me to get a greater understanding of Neural Networks. I learned how to enhance the performance of Neural Network by selecting appropriate hyperparameters, using regularization, using normalization and various other techniques. It was interesting to learn about the Softmax function.

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.

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...

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.

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.

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.

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.

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.

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.

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!

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.

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

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.

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.

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.

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.

Extremely clear and informative about deep learning algorithms per se. The only issue I had is the Tensorflow exercises: since I had never seen TensorFlow before, I lost time guessing the syntax. A more progressive exercise sheet would help get familiar. The point is that, by having to focus so much on the syntax, one focuses less on the structure of the language, so one learns less well the ideas behind the TensorFlow design.

This course gives you a better understanding of how to increase the performance of your neural network.

There are some video-lectures that are a little harder to understand and maybe boring but, in general, I recomend this course.

Este curso te da un mejor entendimiento de como aumentar el rendimiento de tu red neuronal.

Hay algunos videos que son dificiles de seguir y quizas aburridos pero en general recomiendo hacer este curso.

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.

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.

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.

Great course and great Professor to teach from - very well explained all the materiel. The quiz are very good and really test understanding. There is a place to try to make the programming exercise less guided to the last detail and a little more to let people think about how certain things need to be done and send them to look at documentation (a good balance of right guidance and hard work seems like great recipe).

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.

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.