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返回到 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

學生對 deeplearning.ai 提供的 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 的評價和反饋

4.9
42,366 個評分
4,520 條評論

課程概述

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....

熱門審閱

XG

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.

NA

Jan 14, 2020

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

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101 - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 的 125 個評論(共 4,456 個)

創建者 Mehedi H

Sep 24, 2017

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

創建者 Lyle T

Sep 15, 2017

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

創建者 Jingyu Z

Jul 15, 2019

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.

創建者 Marlon A C V

Sep 28, 2017

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!!

創建者 Kyle W

Aug 15, 2017

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.

創建者 Rohit K

Jul 06, 2019

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.

創建者 Itsido C A

Dec 17, 2019

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.

創建者 sunshineren

Aug 31, 2019

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!

創建者 Nouroz R A

Sep 13, 2017

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.

創建者 Rohit

Jul 06, 2018

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.

創建者 XiaoLong L

Aug 14, 2017

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.

創建者 Ram N

Jan 01, 2020

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.

創建者 Harry ( D

Jul 21, 2018

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.

創建者 Mukund C

Oct 15, 2019

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.

創建者 Ayush K

Jun 16, 2018

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.

創建者 colinyu

Jan 15, 2018

Prof Ng is a great teacher and is good at making the difficult material very easy to learn. I am very interested in the DL. Before I took this class, I found that since this field is very new so all the material you can find is a little piece and not systematical. This specialization is a wonderful and systematical, easy to learn and fun. Thanks for the great work those teacher have done .

創建者 Zhou S

Mar 08, 2018

Awesome illustration on deep network's regularization techniques, weight initialization techniques and gradient checking, and more. This class provides you with hands-on experience with how to tune a deep network efficiently. You will not only learn the techniques but also understand many of the intuitions of how each technique works. A must take if you are dedicated into machine learning!

創建者 Rusty M

Dec 07, 2018

I learned a lot about the area that is not much talked about in deep learning, which is hyperparameter tuning! The forum was very helpful in debugging the programming assignments! Thank you Prof. Ng for the wonderful course. I thank Coursera as well for believing in me and granting me Financial Aid. It wouldn't have been possible without your help, Coursera Team. THANK YOU VERY MUCH! :D

創建者 Neeraj B

Oct 03, 2019

This was an excellent follow-up of the first course. Having used adam optimization for almost all the neural network models I have build it was great to understand the mathematical intuition behind adam optimizers. Also the programming assignment gave a wonderful refresher and practice of tensorflow. Overall I'm glad hyperparameter tuning and optimization was chosen as a seperate course

創建者 MANRAJ S C

Oct 16, 2019

The course is great and will help you in understanding on how to optimize your deep learning algorithm and tune your hyper-parameters. The course provides insights into the exponentially weighted averages concept too which helps you understand how things work behind the scenes when trying to optimize your algorithm. Dropout and regularization have also been explained to a good extent.

創建者 Chan-Se-Yeun

May 01, 2018

This course is very useful for practical purpose. I've learnt a systematic method to develop and iterate my algorithms, which saves me a lot of time. And it's been the first time that I get to know so many variants of gradient descent method, such as Adam and RMSprop. By the way, the programming assignments get a bit hard, but it help me better understand the algorithms. Thanks a lot!

創建者 Andreea A

Feb 02, 2019

This was a useful course for newbies in neural networks. It gave useful hints regarding how to update the model one is using based on what problems one observes, as well as how to tune the hyperparameters (if there is enough computational power or one runs a small problem). Obviously, this is just a starting point and one should invest a lot of time and energy to become experienced.

創建者 Jay G

Sep 24, 2018

All the quality of the first course, but even better. My 4-stars for course one were addressed in these Jupyter notebooks. They were still manageable but the prompts provided very good reinforcement to the various tuning algorithms. A top-notch offering...one I'll be sure to recommend broadly. I'm very much looking forward to the remaining courses in the Specialization. Thanks!

創建者 Sarthak k

Aug 12, 2019

I had a very good time getting teaching sessions from ANDREW NG .., I am a second year student and have entered in this field of deep learning since some months then i encountered this specialization and with the deep concepts of Sir ANDREW NG ,i am now able to make much more complicated models ever before...I hope i could get an autograph from my Ideal in this field

Mr.Andrew Ng

創建者 SUJITH V

Oct 26, 2018

This is a great course to learn about practical aspects of neural networks. Some parts are challenging to consume as most of the material relies on intuition rather than detailed mathematical explanation. This helps to involve more people in the course who are intimidated by mathematical equations. A great addition would be to have optional mathematical details in separate videos.