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

deeplearning.ai의 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 학습자 리뷰 및 피드백

4.9
별점
52,660개의 평가
5,958개의 리뷰

강좌 소개

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....

최상위 리뷰

CV

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.

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.

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 5,898개 리뷰 중 26~50

교육 기관: Ethan G

Oct 17, 2017

I did not think this was a great course, especially since it's paid. The programming assignment notebooks are very buggy and the course mentors are of varying quality. It feels more than a bit unfinished. It also covers two completely different topics - tools for improving deep learning nets and tensorflow - and doesn't make much of an effort to integrate them at all. The course could have used at least one more week of content and assignments to better explain the point of tf.

교육 기관: Hequn W

Apr 18, 2018

I can't open week1 assignment initialization, and can't get any help from coursera. I've completed all five courses, but can't get certification without this assignment....

교육 기관: Kunjan S M

Jun 12, 2020

Hello. I am Kunjan Mhaske, a graduate student of Computer Science completing (fingers crossed!) the MS degree in December 2020. Currently, I do not have funds to take university courses till August and hopefully, I could secure the co-op or internship for August to December 2020 so that I can fund my remaining semester from it. I am interested in AI and Data Science field and currently, my major is in AI with Computer vision and Machine Learning. I heard very positive reviews about this Specialization course of Deep Learning from my friends and I wanted to complete it this summer so that I could cash this knowledge in my full-time job or internship hunt. Please refer me for any opportunity I am very much in need of financial support for the completion of MS degree as well as living costs. My email is km1556@rit.edu and https://www.linkedin.com/in/kunjan-mhaske is my Linkedin profile. Fortunately, Coursera offered me the financial aid for the first and second courses in this specialization which turns out to be very helpful for me in this situation. I have applied for the rest of the courses in this specialization. Hopefully, I could get financial aid for all the 5 courses. Although I have a 3.76/4.0 GPA, the depth of concepts explained in this course is very good for my level. I have completed this course in 6 days and I am already feeling confident about the field of Deep Learning. Thank you so much for this wonderful course material and your support. God bless you all.

교육 기관: Vinod K

Jan 16, 2018

I had taken Andrew Ng's Machine Learning course. I went on to learn Deep learning from other tutorials and I always wished there was a course on Deep learning too by Andrew Ng. And now that there is, It was worth the wait.

1. All the topics are arranged in logical order. So you feel like a tour of deep learning. Earlier I had to refer to multiple sources for different topics and they usually had different naming and notations which were really confusing.

2. Having taken about 6 top rated courses on AI domain, I can assure you Andrew Ng is the best in his teaching style and content.

3. Exercises and theory go hand in hand. So, you know how to implement as soon as you learn theory.

4. Out of a lot of techniques in each topics like Optimization, Regularization etc. this course picks the most contemporary techniques. This helps you not to wonder which techniques to use in your work.

Overall, This Specialization is like a cookbook for AI. My appreciation and gratitude to Andrew Ng and his team for their contribution to AI.

교육 기관: Shibhikkiran D

Jul 08, 2019

First of all, I thank Professor Andrew Ng for offering this high quality "Deep Learning" specialization. This specialization helped me overall to gain a solid fundamentals and strong intuition about building blocks of Neural Networks. I'm looking forward to have a next level course on top of this track. Thanks again, Sir!

I strongly recommend this specialization for anyone who wish get their hands dirty and wants to understand what really happens under the hood of Neural networks with some curiosity.

Some of the key factors that differentiate this specialization from other specialization course:

1. Concepts are laid from ground up (i.e you to got to build models using basic numpy/pandas/python and then all the way up using tensorflow and keras etc)

2. Programming Assignments at end of each week on every course.

3. Reference to influential research papers on each topics and guidance provided to study those articles.

4. Motivation talks from few great leaders and scientist from Deep Learning field/community.

교육 기관: Weinan L

Feb 05, 2018

Used to tune hyper parameters based on experience... after this course, know more about the internals and from now on, not just know HOW to tune, but WHY it needs to tune this way.

As always, Andrew did fantastic work here to help explain complex formulas in simple and CLEAR way.

Highly recommend it to anyone who fight with overfitting, hyper parameters tuning, etc. It may not help you instantly become a better AI person or help you immediately help you on your day to day programming - as you most likely use various frameworks (Keras/TensorFLow/PyTorch) instead of raw NumPy. But it does help you in the long with better knowledge. It is kinda like show you how the engine works, before teach you more driving skills. It won't help you when your car is working fine, but when it breaks, you know how to troubleshoot and what is the right direction to go. Honestly, I personally think the debugging part is the toughest part of AI.

Take it. Period.

교육 기관: Zeyad O

Apr 15, 2020

I'm Zeyad, an undergraduate of Computer Engineering at Alexandria University in Egypt.

Taking this course really helped me to learn and study this field and also to implement it. It helped me advance in my knowledge. This course helped me defining Deep Learning field, understanding how Deep Learning could potentially impact our business and industry to write a thought leadership piece regarding use cases and industry potential of Machine Learning.

This specialization helped me identifying which aspects of Deep Learning field seem most important and relevant to us, apparently they were all important to us. Walking away with a strong foundation in where Deep Learning is going, what it does, and how to prepare for it.

Deep Learning specialization helped me achieving a good learning and knowledge about that field.

Thank you so much for offering such wonderful piece of art.

Best Regards,

Zeyad

교육 기관: Anirudh K

May 08, 2020

This is a really informative course and really crucial if you are planning to do a personal project or even prepare for interviews. It equips you with all the tools to get started with actually start implementing Neural Networks for a problem by 1) Teaching how to prepare data sets 2) Regularization/dropout to increase accuracy on test set, 3) Set up your optimization problem 4) Teach different Optimization Algos 5) Teach Hyperparameter tuning and the order of importance of different hyperparameters 6) batch Normalization and lastly Tensorflow. Andrew NG is truly a master in teaching concepts in an approachable and intuitive way. I believe the course can be made even better by adding Keras to the programming frameworks module along with more videos and programming exercises for data pre processing.

교육 기관: Nigel S

Jun 10, 2019

It explains a bunch of complicated maths and methods in a way that is at least comprehensible by mere mortals, though not necessarily easy. Put another way, if this course doesn't enable you to understand how to tune and optimise deep neural networks, then you probably never will.

The content taught in this course is really valuable because it explains a lot of what is going on behind the scenes in the existing Deep Learning Frameworks like Tensorflow, Keras, etc, and enables you to be a lot more competent and confident in producing effective models in a time-efficient way, than if you didn't have this knowledge.

It also seems to have been built by peopel who not only know the material intimately, but who recognise that many of the learners are very time-poor.

교육 기관: Taylor B

Jun 23, 2019

I took the Machine Learning Course from Stanford with Andrew Ng a few years ago and enjoyed it but I was also somewhat overwhelmed by the math. In contrast, this is my second course in the deep learning specialization and I feel like so far the courses have struck a good balance, introducing core concepts and derivations for things but also making sure I get guided practice along the way, and also not moving straight to frameworks but having students code more or less from scratch first. I'll probably need some practice on kaggle or other datasets as well as reference to a few other learning materials to feel like a strong practitioner, but this gives the tools to make that possible and I'm very satisfied with this result.

교육 기관: Jorge L

Feb 17, 2019

All the courses in the Deep Learning Specialization are very good and met my expectations. I was guided through the nitty-gritties of neural networks, fortunately with a strong emphasis on Computer Vision (my area), deep diving in coherent coding exercises. Prof Andrew, as always, managed to connect the points between theory and practice, recollecting the concepts treated in past lectures, while showing how Tensorflow operates and how to use it. If you ask me, I'd say that the slides of the Machine Learning course used to be better than the slides for the 4 courses in this specialization, in the sense of being useful as studying guide for the future. The current slides only make sense to those who went through the course.

교육 기관: Luca C

Jan 27, 2019

Knowing this makes the difference. How do you evolve from being a monkey behind a keyboard knowing how to tensorflow a NN to homo sapiens? The concepts provided in this course will make the job.

pros: + workflow to address and optimize your supervised learning problems

+ wide and easy-to-get overview on most essential concepts

+ improves your understanding of NN; those who are already familiar with these concepts might still benefit from this clear and insightfull presentation

cons: - programming assignment will not suffices to give you a sufficient knowledge of tensorflow to make your own applications, you should integrate a bit. (However, mastering tensorflow is not the intention of the assignment).

교육 기관: Baohe Z

Oct 05, 2017

Good pace for beginner as the last one. With step by step teaching us a lot of useful skills to train our model much faster, Andrew starts to put more attention on practical field, and rather than giving us many equations, he as before likes to use some vivid examples for giving us an intuition, which I think is very helpful to understand those scientific words of computer science. But it doesn't mean, that this course is perfect, even I gave a full point to it. The subtitles have a lot of mistakes and the audio is also poorly processed. Sometimes, you will hear the same words twice.

But in a word, this is the best course for the beginners and the engineers who are willing to know something about ML and AI.

교육 기관: Amilkar A H M

Nov 24, 2018

I loved it. It showed me practical aspects of machine learning, including how to chose the hyperparameters and how to use tensor flow. My only complain is that I'm not sure how much of this information I will retain given that the practical exercises are guided. They build a lot of the functions for you. Still I'm giving it 5 stars because I have not seen this problem solved so far in any other Coursera course. They need to find a balance between teaching you a lot and making it easy enough for most people to be able to pass the exam and not get stuck in the details. Probably they could offer extra practice automatically graded exercises for those of us who want to make sure we won't forget the material.

교육 기관: Victoria D

Nov 25, 2019

I'd highly recommend this course to any of my colleagues interested in Deep Learning.

It is a great followup to Deep Learning and Neural Networks.

My one 'complaint' is that the mathematical depth is too shallow for someone like me (PhD, Mathematical, Computational and Experimental Physics)

It would be great if there was a course that was targeted to people with advanced STEM degrees, and years and years ( 4 decades in my case) of software engineering experience, where more time was spent on the mathematical framework, and the basic algorithms; that way, I'd have the satisfaction and pleasure of constructing the full algorithm implementations myself.

That being said, once again, Andrew is a great teacher.

교육 기관: Matthew J C

Feb 28, 2018

I was very impressed with the quality of Dr.Ng's teaching; simple enough to build confidence in your understanding of the inner workings of neural networks yet thorough enough to prepare you for deeper study (academic or otherwise). For $50 this course is a steal; you could go gather all the information & struggle through it yourself but be prepared to spend a lot of time & effort sifting through mis-information.

After taking the 1st coarse I was impressed; course 2 follows in a similar vain. Some of the courses offered through Coursera are more polished than others; if you're at all curious in deep learning, or even if you've already begun your studies, do NOT miss out on this opportunity.

교육 기관: Francis S

Aug 26, 2019

Previously, I have taken online classes before in Machine Learning by going the cheap route (Udemy, blogs, youtube) and you get what you pay for. Andrew Ng explains it the most thorough, easiest, and simplest way possible. Presentation material is very understandable. Great class for new machine learning learners. Highly recommend it. The only downside is that the programming exercises are little too easy in my opinion. I feel like the best way to get your hands dirty is to do actual projects (do your own projects). These lectures are good for intuition and background of different types of Neural Network architectures. Other than that, Great material. Thanks Andrew!

교육 기관: Emilio J

Mar 20, 2019

El curso está muy bien impartido por Andrew NG y te permite adquirir muy rápido conocimientos sobre los puntos clave para mejorar el aprendizaje con redes neuronales de una forma genérica. La práctica de programación con la plataforma tensorflow de python es muy valiosa, aunque se hecha de menos una mayor profundidad en el uso de las herramientas disponibles de tensorflow y otras utilidades de python para redes neuronales. El curso utiliza como ejemplos didácticos y prácticas la aplicación de redes neuronales al reconocimiento de imágnes, pero estaría bien ampliar los ejemplos con aplicaciones prácticas a otros campos como puede ser un modelado de un proceso físico.

교육 기관: Utkarsh V

Jun 09, 2020

This course has been the game-changer in my understanding of the concepts of hyperparameter tuning and optimization. The conceptual knowledge of various tuning techniques along with the theoretical and practical information about the algorithms like GD with Momentum, RMSprop, and Adams have made me confident.

The introduction to the machine learning framework along with the assignment focusing on Tensorflow has also made me confident to learn more and prepare better projects.

As I have mentioned in my previous course review as well the programming assignments are extremely useful and very much important for the understanding of various deep learning concepts.

교육 기관: Adam S

Feb 07, 2020

Andrew ng is simply the best. He is by far the number one teacher for ML. He explains the materials in such an intuitive way like no one else. I think that for my needs, he introduces just the right amounts of math and practicality.

This course really showed me how gradient descent optimization methods work. From before, I knew about these optimization methods and what they do, but never fully understood them. After taking this course I feel much more confident using them. The transition to tensorflow is done at the perfect time. After writing NNs yourself using numpy (first course in this specialization), you can truly appreciate tensorflow.

교육 기관: Teguh H

Nov 29, 2017

Great in depth explanation from ground up on how to tune parameters. Including many personal experience by Andrew Ng throughout years of experience of handling AI projects. Before going into quick shortcut by using the Tensorflow libraries, it is really useful to know the concepts and intuition on how Deep Learning works from ground up. Also teaches you how to solve with many problems in overfitting, underfit, reading the results. In short, his experience that has seen many researches spent too much time into creating projects, and end up hitting brick walls, is summarised with suggestion on how you can avoid that in your AI project.

교육 기관: Gerardo M L

Jun 18, 2019

The course is amazing, the instructor explains everything with a good level of comprehension. All the covered topics are easy to understand, and the tips given are valuable. The examples given are new including also the information seen in the previous course, so you have a review of parts of the content you have seen. Although he keeps using the cat example, he introduces new other applications that are useful.

I wish that the last assignment were a little bit harder, or that we could use our previous knowledge and complement it with this new, but I suppose that it is this way because of pedagogy and it focus on the topic.

교육 기관: Jason J D

Aug 06, 2019

This course is wonderful! Hats off to Prof. Andrew. The explanation for each topic is step wise and well organized. Every detail and reasoning is covered up. Even though there is a lot of content in this course, it is easy to remember and understand most of it, because of the way it is explained. The programming exercises are well planned and guide you through the code well. This course also has a brief introduction to TensorFlow, which is explained well through its programming exercise. Overall, this course is really good for those who are looking to master the methods to improve and optimize Neural Networks.

교육 기관: Maximiliano B

Oct 27, 2019

The second module of the deep learning specialization is excellent. You will learn best practices regarding hyper-parameter tuning, how regularization can be used in Neural Networks, optimization algorithms such as Momentum, RMSProp and Adam. In addition, you will be able to build your first machine model using tensor flow as part of the practical assignments. Professor Andre NG explains the content clearly and it is very pleasant to watch his lectures. I definitely recommend this course because it will give you confidence to build your own models and will provide several additional tools in your tool-belt.

교육 기관: Carson W

Jan 04, 2018

As with the first course in this specialization, the presentation was spot-on and the content was rich. The practical application is a wonderful tool for learning and I feel as though I have learned much more than I might have in a traditional classroom. I also feel that this course was slightly more challenging than the first, and introduced me to a few concepts I hadn't heard of before despite other research and development in AI/ML. Thank you so much for your dedication to sharing your knowledge and introducing new students to some of the brightest minds in the field with the optional interview videos.