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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
57,409 個評分
6,595 條評論

課程概述

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

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AM
2019年10月8日

I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation

XG
2017年10月30日

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.

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5576 - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 的 5600 個評論(共 6,521 個)

創建者 Johan W

2017年10月30日

Starting to get somewhere.. This course contains a bit more interesting topics and I did not feel it was as overlapping to the Ng Machine Lerning course as the first course in the Deep Learning specialization was. Also, finally, a programming task is done using a deep learning framework (TensorFlow)

創建者 John S

2018年11月11日

Good material as always from Andrew Ng. There is a lot more "eyes-forward lectures" before the coursework on this one, which didn't suit my my personal learning preference as well (I prefer to get a lot of practise in when getting to grips with new concepts). Good course though, worth the effort.

創建者 Andrei M

2018年4月1日

I feel like the assignments involve a lot of cut and pasting of functions verbatim. It's a good start, but I'd like to go further and be challenged to solve a problem, rather than fill in the blanks. For example, try different optimizers to reach a particular learning speed on a given data set.

創建者 ehsan

2020年6月16日

Everything was fine but there were some issues that was not pleasant to me. like seeing that there's problemmes with the videos and they're not corrected and instead there's an extra reading about that. Or when Tensorflow 2 has come, I was expecting that the course also introduces newer version.

創建者 Parham A

2020年8月13日

The course and the instructor are amazing, but I fell behind schedule by one week and the last assignment was locked, and when attempting to reset the deadline, a message saying "something went wrong" would pop up. The help center responded very quickly and professionally, and solved the issue.

創建者 Dhruv S

2020年6月8日

This course I believe is one of the most vital one after the first course in the specialization! Professor Ng covers all the concepts required for you to understand and master this course.

You might have to refer to additional resources to get a complete grasp of the concept post each video.

創建者 Eemeli L

2019年11月19日

Great and easy-to-follow introduction to improving deep neural networks. If you are already familiar with vector algebra, many things are explained quite slowly. One star left out because the content has not been polished, but there are minor errors here and there with separate corrections.

創建者 Krishna k N

2019年6月4日

Going from the Basics of Logistic regression-Neural network -regularization- hyperparameter selection and finally knowing how Tensor flow makes it all come together is just brilliant.

I feel confident as I understand the basics well before using a framework that makes it so easy to execute.

創建者 Basel A

2018年8月5日

Realy recommended for those who finished the first course and/or the machine learning with Prof Andrew Ng. A good in deep exploration of different topics in regularization. An efficient introduction to TensorFlow which will put your feet on the first step of using DeepLearning platforms.

創建者 Kevin W

2017年10月2日

Need some improvement! I think the course is a little bit rush, especially on the 3rd week. I really like the 'test' assignments, which helps me to clear out a lot of important concepts. But the programming assignments sometimes bothers me not in the way of programming, but in the way of

創建者 Hari K

2020年10月6日

Excellent! Very practical intro to tuning and improving neural networks. The four star is because I felt the programming exercises could be harder and not so much fill in the blanks. I don't think I would be able to code a adam optimizer or momentum from scratch given an empty py file.

創建者 Juan A O G

2018年9月4日

A great course indeed! I give 4 star only because I'd have liked more lectures and programming exercises about Tensorflow, and how to train models using GPUs. Similarly, it'd have been great if Andrew explained in detail how to implement Batch normalization using computational graphs.

創建者 Joseph A D

2018年1月13日

Great course. Thanks for making it available.

I would have enjoyed more tensorflow lectures to help understand the underlying mechanism of the platform. I suppose the intention is to provide that understanding through the assignment, but more discussion in the lecture would be nice.

創建者 Nicolas M

2018年3月20日

Good course but it would be interesting to add some other methodologies on learning rate ("Cyclical Learning Rates for Training Neural Networks", "Snapshot ensembles") and some explanations on categorical variables and embeddings matrix ("Entity Embeddings of Categorical Variables")

創建者 Eloi T P

2017年9月16日

Great course giving insight on how to fine tune deep neural networks. I believe the contents need to be a bit polished but that's totally understandable given its early stage. The comments in the discussion group will for sure help to fix some typos and make this course even better.

創建者 Gem D

2021年3月6日

This course helps me a lot in tuning hyperparameters in training machine learning model, just one issue is the last programming exercise when using framework the guide is missing something, which is hard for some to complete, for me, I have to use Google Search to find the solution

創建者 Siddharth K

2019年7月15日

Need Information about other parameters like #of iterations, how to choose number of hidden layers?, number of neurons in hidden layers, inclusion of few other strategies to choose neural network model will be helpful. If they are covered in next courses, then please ignore.

Thanks

創建者 Sothiro P

2018年8月5日

A useful class delving into the nuts and bolts of building a reliable nn. Well structured and explained. I feel like the use of Jupyter in the homework makes it simpler than it should be. A large portion of the code is already written and the instructions often give up the answer.

創建者 Mathieu B

2020年7月11日

For a person, who know a little on deep learning, I learned lots of things or, at least, got a clearer view on many concepts. A little reproach on the notation system : question on quizz sometimes might not be very clear for me - and the flaws of the grader on the assignments.

創建者 Shankar S P

2018年12月8日

A very good course for taking understanding of Deep learning one level above the basics. The course is theoretical, but the team has done their best to make it as much hands-on as possible.

I did face some intermittent platform issues with saving and submitting my assignments.

創建者 Ramprakash V

2020年8月3日

Helps to have a structured approach towards tuning the hyperparameters rather than randomly doing. Also the course also helps understanding why such tuning is necessary and what improvements are being made in the model. Useful course but not suitable for beginners in ML/DL.

創建者 Vasilii D

2019年12月23日

Material is awesome like all courses professor Andrew does. But (a) programming assignments are in style 'fill a couple of lines in 90% ready code' instead of end-to-end developing with guidelines and (b) there are a lot of mistakes in subtitles, assignments and even videos

創建者 Varun K M

2020年5月19日

A lot of content was repeated from the Machine Learning course by Andrew Ng on Coursera. Also, more on TensorFlow and other frameworks implementation would be interesting to learn. But at the end of the day, I did learn a lot of interesting aspects of deep neural networks.

創建者 Maciej B

2017年8月22日

Course is very good especially when revealing "secrets" of various optimization techniques. Once again programming excercise is rather easy to pass as you are guided step by step so there is no space for serious mistakes. More "open" excercises/chalenges would be desirable

創建者 Ruchita R B

2020年7月20日

This one took a little longer than usual to complete, It took more willpower to come back to it and continue in the course. It seemed harder, or explained lesser than the first course. Nevertheless, after spending extra time on it, Ive finally completed it. Thanks Andrew!