Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization(으)로 돌아가기

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4,502개의 리뷰

This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.
After 3 weeks, you will:
- Understand industry best-practices for building deep learning applications.
- Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
- Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
- Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
- Be able to implement a neural network in TensorFlow.
This is the second course of the Deep Learning Specialization....

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.

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.

필터링 기준:

교육 기관: 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.

교육 기관: 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.

교육 기관: 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).

교육 기관: 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.

교육 기관: 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...

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: 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!

교육 기관: 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

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: Jean T

•Feb 07, 2018

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.

교육 기관: David R R

•Nov 15, 2017

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.

교육 기관: Sikang B

•Apr 01, 2018

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.

교육 기관: Durgaprasad

•Jan 20, 2020

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.

교육 기관: David S B

•Jul 24, 2019

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.

교육 기관: Heinz D

•Nov 06, 2019

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.

교육 기관: Jaime M M

•May 29, 2019

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.

교육 기관: Renzo B

•Aug 28, 2019

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.

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