Chevron Left
Вернуться к Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Отзывы учащихся о курсе Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization от партнера deeplearning.ai

4.9
звезд
Оценки: 55,748
Рецензии: 6,378

О курсе

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....

Лучшие рецензии

XG
30 окт. 2017 г.

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.

AS
18 апр. 2020 г.

Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course

Фильтр по:

126–150 из 6,304 отзывов о курсе Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

автор: David B

24 июля 2019 г.

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.

автор: Heinz D

6 нояб. 2019 г.

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.

автор: Jaime M

29 мая 2019 г.

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.

автор: Renzo B

27 авг. 2019 г.

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.

автор: Mehedi H

23 сент. 2017 г.

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

автор: Mikhail G

7 мар. 2020 г.

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

автор: Chong O K

31 окт. 2020 г.

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.

автор: Ganesh S V M K

2 авг. 2020 г.

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 :)

автор: Lyle T

14 сент. 2017 г.

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

автор: Jonathan M

1 мая 2020 г.

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.

автор: Jingyu Z

15 июля 2019 г.

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.

автор: Sakshar C

17 мая 2020 г.

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.

автор: Marlon A C V

28 сент. 2017 г.

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!!

автор: Kyle W

15 авг. 2017 г.

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.

автор: Rohit K

6 июля 2019 г.

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.

автор: Itsido C A

16 дек. 2019 г.

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.

автор: sunshineren

31 авг. 2019 г.

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!

автор: Nouroz R A

13 сент. 2017 г.

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.

автор: Rohit

6 июля 2018 г.

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.

автор: XiaoLong L

14 авг. 2017 г.

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.

автор: Anoop P P

5 июня 2020 г.

NIce Course on hyperparameters search and tuning. The optimization functions and its relation to the hyperparameters is well taught. Mini-bacth normalization during training and application of learned parameters in testing is discussed very well. At last, deep learning frameworks were introduced and the practical training on tensorflow framework was awesome. Thaks for the well designed content.

автор: Ram N

1 янв. 2020 г.

The course covers the theory and implementation details of advanced optimization algorithms. A good amount of intuition was provided in the explanation of these algorithms. A basic explanation of bias and variance and how hyper parameters affect them both is explained clearly. I liked the hands on part, as it allowed me to implement the algorithms discussed and gain more clarity in the process.

автор: Harry ( D

20 июля 2018 г.

Very useful follow up to the first course in this specialization. Learned all the details of how to tune and optimize a deep neural network, as well as nice introduction to Tensorflow. Some typos in the comments of the final assignments but they were easy to spot. This time Jupiter notebooks worked better that during the time I was working on the previous course with less or no resets required.

автор: Mukund C

14 окт. 2019 г.

Excellent Course. Really structured way of learning the importance of hyper parameters and their effects on the learning/training and hammering concepts like "regularization" home.

Just an observations, but it seems like the mentors are not that engaged in these courses anymore, but there are enough help threads that one can figure out the questions - specifically on the programming exercises.

автор: Ayush K

16 июня 2018 г.

What an amazing course it is. Perfect explanation how we can use optimize our cost more efficiently and effectively. Also this course includes techniques to overcome problems like over fitting i.e Regularization and Dropout techniques. Information about Batch Normalization is very splendid. Also got little intuition about tensor flow. Thank You Andrew Ng for providing such a wonderful course.