I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse
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 Ivanovitch S•
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•
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•
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•
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 P•
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.
by Jean T•
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.
by David R R•
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.
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!
by Durgaprasad N•
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 Itay M•
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).
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.