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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
57,892 個評分
6,657 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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JS
2021年4月4日

Fantastic course and although it guides you through the course (and may feel less challenging to some) it provides all the building blocks for you to latter apply them to your own interesting project.

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

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5601 - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 的 5625 個評論(共 6,584 個)

創建者 Emmanuel T

2019年10月3日

Compared to previous module, this one was more of a cookbook and I expected more mathematics in terms of why each optimization work.

Overall, it was still a very interesting hands on approach, finishing with TensorFlow is a bit more difficult to apprehend as all the previous exercices were done in a very different way (Numpy).

創建者 Pawel P

2020年12月9日

Most of the course is great, good overview of different methods and techniques with practical examples. However the TensorFlow programming part is rather confusing, lacking in sufficient explanation of the syntax and overlapping names of python and tensorflow variables which end up producing near impossible to debug errors.

創建者 Girish G

2020年4月13日

This is an amazing course which dwells into the nuances of fine tuning your neural network model. The content of the course is too good. Programming assignments was a bit off. It was really straightforward. Programming assignments could have been more challenging. This will make sure that the concepts are learned properly.

創建者 Le H L

2018年6月10日

The content is generally great and helpful, but the grader did not show me why the result is incorrect, and i constantly had to reload jupyter notebook. I think there should be less template for the exercise so that we have more thinking to do, but the expected result should be maintained so that we know what we did wrong.

創建者 Rakesh S

2017年8月31日

The course explains the reasons and intuition behind tuning hyperparameters and why/how regularization techniques work well when training on large data sets. The only reason I am giving this a 4 star is because the tensorflow introduction seems a little too sparse and could be done better.

Thanks again, team deeplearning.ai

創建者 Juan P A A

2020年1月27日

The contents are actually good, and it doesn't require a very extensive prior knowledge, so it's even suitable for people with little experience in programming or math. However, despite being a course that has been out for over 2 years, there are still some subtitle issues (in English), and typos on a clarification slide.

創建者 John H

2017年8月24日

Well explained..sometimes jumps a bit. I felt lost a couple of times. But I got through it and I'd say this is deifnitely one of the top courses out there.

If they included some optional videos on how this could relate to having a career in this area that'd be very helpful (i.e. what level we need to be able to code at).

創建者 Anmol K

2020年6月16日

This course continues to build on foundations from course 1 of the specialization. Hyperparameter tuning and Regularization methods are quite imperative for optimizing ML models. This course covers these concepts in addition to providing a good foundation for Tensorflow library. Overall, a good course by Prof. Andrew!

創建者 Екатерина Р

2019年7月24日

The course was very helpful as now I understand optimization techniques and all the parameters of neural networks. Unfortunately, the course has not answered my question how to tune the whole bunch of hyperparameters from the scratch, what is the correct order and logic of the full ANN tuning, not just one parameter.

創建者 Srinivas K R

2017年9月22日

A very good follow on to the first course with continued excellent organization and hands on assignments that give you practical exposure to working on deep learning problems including a basic introduction to Tensorflow along with practical guidelines on Hyperparameter tuning among other deep learning related topics.

創建者 Udaya R

2018年3月11日

Hi,

This course does a really good job in introducing the optimization techniques. Prof. Andrew Ng has structured his lectures well.

Can I kindly suggest that this course can incorporate, for each optimization, 1 scenario that is applicable & 1 that isn't? That will emphasize the scope of the optimization.

Thanks,

Uday

創建者 Shawn W

2017年10月2日

Still some errors in the assignments, particularly in Week 3. Otherwise, a good course. A lot of good topics for practical use when building real-life neural networks. Some seem fairly cutting-edge. Good, brief, introduction to TensorFlow at the tail-end of Week 3 and in the programming assignment for that week.

創建者 Raghav B

2018年12月26日

The course content is really great and the theoretical concepts (or their intuition to a larger extent) have been explained pretty well. But there are some errors in the programming exercise on Tensor Flow which makes it confusing since the people who take this course are new to both deep learning and tensor flow.

創建者 Chris A

2018年4月29日

This is a great course. The only reason for not giving it 5 stars is the notebook platform for grading coding assignments. It is flawed in that attempts to save often error out so a submission often doesn't contain the latest state of the code. This causes sections to be graded incorrectly - very frustrating.

創建者 Dmitrijs T

2020年1月10日

Course material and Lectures is perfect!

May be would like to have less supervised programming assignments with less hints of how to implement code as it was too easy! May be it would be good to have pretty guided assignments during main part of the course, but something more individual and demanding at the end.

創建者 Carles S F

2018年5月28日

I think it is important to understand the basics and that is why it is really cool that they show you how to implement a neural net from sratch. Moreover, the last part on tensorflow shows you how to do it in real life. Nevetheless, a lot of work remains to be done to learn properly how to use tensorflow and NN

創建者 John H

2020年5月8日

Andrew Ng is great at explaining the theory and practical aspects of DL concepts. I applaud him for making the lectures so accessible. I also really liked that he provided what is typically done by practitioners. My only feedback I have is that the quiz and programming assignment could have been more rigorous.

創建者 Yunlin Z

2017年9月7日

the programming assignments are too easy. Though I understand that they're supposed to guide someone who may be a total beginning in ML and DL, I feel there is still too much hand-holding by marking exactly the changes need to be done and the formula either embedded in the comments or in the description above

創建者 Bhavul G

2018年4月3日

Brilliant. The way this whole course was structured, the correctness of everything, and the amount of thought and preparation that has gone into this is amazing. Thank you.

One suggestion : for those who would want to fiddle with math, there could be optional links to read / understand derivations and stuff.

創建者 Anirudh R

2020年6月6日

I understood in detail about the various hyper parameters and how to tune them in order to obtain the best results. I learnt about the problem of over fitting and how to solve it. I also got a glimpse of the tensor flow framework and understood how it makes the process of creating neural networks easier.

創建者 Austin G

2018年12月15日

Great until the last weeks exercise. It definitely needs to be improved. Some of the instructions do not make sense and there are errors in the outputs that make it confusing to follow. However, I did complete it in 1 hour where it said it would take 3. That being said, all videos were amazing as always.

創建者 Jatin s

2020年7月27日

The content for the first two weeks can be thoroughly practised using the programming assignments but for the 3rd week there is a huge gap in the practise material provided.The majority of the content taught during this week is not covered in the programming assignment.Hope this gets seen and rectified.

創建者 Minseok L

2018年1月31日

While the first course in Deep Learning Specialization gives us the fundamental insight, this course shows us the practical aspects such as hyper-parameters tuning and mini-batching . I think it is essential for beginners to take this course if they actually want to apply Deep Learning to their domains.

創建者 李欣宇

2020年7月9日

Generally great course! But I think more tutorials about Tensorflow are needed. I suppose by using the current tutorial in this course, no one except those who have used Tensorflow before, is able to apply Tensorflow to their own work. The programming exercise is more like a

shallow experience so far.

創建者 Ali K

2020年2月27日

The course was useful for me as a beginner in the deep-learning domain. The instructor was very clear and easy to understand. In order for the student to easily grasp some concepts, useful intuitions were provided. However, being a mathematics student I would have appreciated a more thorough analysis.