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

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Оценки: 59,781
Рецензии: 6,917

О курсе

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....

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.

CV
23 дек. 2017 г.

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.

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176–200 из 6,860 отзывов о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

автор: sujith

26 окт. 2018 г.

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.

автор: Shangjin T

2 мар. 2018 г.

I've learnt much from course including preprocessing (mini-batch, regularization, normalization), gradient descent algorithm (batch gradient descent, stochastic gradient descent, mini-batch gradient descent) and the variants (momentum, RMSProp, Adam). Also there's TensorFlow tutorials which I love best.

Thanks for Andrew Ng for bringing us such an amazing fundamental course of DNN!

автор: Aakash K S

30 окт. 2020 г.

Very well structured and thorough course. Instructor did a very good job in teaching the topics of NN such as Regularization, Optimization etc. and explaining the mathematical concepts such as moving average.

Lab made coding assignments easy to understand and code. Lab made it easy for me to understand the structure of NN and how to code clean NN functions for easy implementation.

автор: Adam D

21 мар. 2021 г.

Excellent! This was number two in the series and both courses so far have been excellent. That notation and frameworks are consistent and build nicely. The exercises, quizes and programming assignments really reinforce the material.

This course is true to its title and one you should definitely take if you want to understand a lot of the nuances of constructing a deep neural net.

автор: Sourabh G

17 окт. 2019 г.

This course really helped me getting the deep insight into the hyper-parameters which need to be tuned to get the optimal learning of the algorithm with the different algorithms necessary for improving learning rate.Andrew Ng really simplified the tough things and arranged them in a proper series of videos that is easy to understand.This will really help me lot in future.Cheers!

автор: Christian H

7 февр. 2021 г.

Great course, but please have the TA proofread the subtitles (the auto-generated ones are bad, if I was deaf, I would have no chance to understand anything) and upgrade to at least 1080p video. After all, since this course costs tuition, there should be a minimum technical standard guaranteed.

But content wise and teaching style wise this is fantastic, thank you so much, Andrew!

автор: Danilo Đ

3 дек. 2017 г.

I suppose Hyperparameter tuning, Regularization and Optimization are some of the most important aspects of Deep Learning, since 90% of most of the DL projects come down to just that. Andrew masterfully dives into the intuitions behind some of the most widely used approaches, and the programing assignments are designed to show the impact good tuning could have on a DL algorithm.

автор: Mohammed A

7 янв. 2018 г.

Great explanation of optimizations that can help speed up deep learning algorithms. Loved the little tips and tricks that are covered in different sections. The easy with which Prof. Ng explains complex concepts and analogies is commendable. The programming assignments are very helpful to people without expert programming experience too, that makes the experience very smooth.

автор: Francisco G A

24 окт. 2020 г.

An Excelent course on learning how to tune hyperparameters and also a peek in using TensorFlow (in the last week, and trust me, it's worth the wating) it's complete, educational and the professor Andrew Ng it's an excelent teacher! I would have liked some more practice on parameter tuning in the programming excercises. But in a nutshell it was a awesome learning experience!

автор: Anirudh S

6 нояб. 2017 г.

In my opinion it would be a good to have a short video describing how to drive the ml project in the company. As i am taking ml course and this specialization, I started with working on octave, then numpy then tensorflow, so it would be good to have some advice/tips on when to use octave or numpy or tensorflow for building a model when you get a project in ml in your job.

автор: Saad A

4 окт. 2017 г.

Post the first course, this course would is the one that is going to make you feel like a deep learning practitioner. You get to understand why deep learning is sometimes called an art how much difference in terms of speed and accuracy can be made just by tuning the hyper parameters. Highly recommended if you know deep neural networks and willing to dive deeper into them.

автор: Xun Y

7 апр. 2019 г.

Again a great course about deep learning. The course structure is very well defined, with step by step to build technical foundations in the beginning and later using open source deep learning framework to connect all the pieces together. Dr. Andrew Ng made all of them very easy to learn and sometimes I feel like I should jump out the comfortable zone he created for us.

автор: Willismar M C

22 мая 2018 г.

Very nice course about important subjects of Vanilla Neural Networks, as optimizations algorithms , regularization methods, hyper-parameters used and how to implement them in practice. A very nice chapter on the sequence of the specialization that give me understanding on important aspects of it, how to use and how to implement them. I really enjoyed each detail of it.

автор: Ahmed S

15 окт. 2021 г.

This was a really challenging course, with lot's of beneficial tips, very well prepared and delivered, I do suggest to send us best practice assignment code after passing each assignment, to be more confident moving forward in the Deep-learning specialization, finally I am really grateful for the effort you are putting behind the Deep-learning specialization courses .

автор: Bharath S

8 июля 2019 г.

This course gives a very good idea of the overfitting problem in deep learning and different ways to overcome it. It also introduces commonly used optimization methods in deep learning. A nice introduction to tensorflow is provided in the last week's programming assignment. Overall it is a very satisfying course. Many thanks to the instructor and the entire team!

автор: Hari K M

3 янв. 2018 г.

Key course in the specialization and covers wide array of topics which are responsible for improving the DNNs. Complicated than the first course but very well explained by Andrew Ng. Things definitely get clear after doing the programming assignments. One should definitely complete this course if one has already completed the first course. I totally recommend it.

автор: Bilal A

12 янв. 2020 г.

Course was amazing, content was amazing, assignments was amazing.

Andrew Ng is the best teacher I have ever experienced in my life. I learned a lot from this course, these things are very difficult to learn from research papers it takes a lot of time but person with great passion of deep learning can learn all these things in just three weeks. Highly Recommended.

автор: Hiep P

29 нояб. 2017 г.

In era of deep learning bloom, know how to control network model is an important thing. And this course has them all, from tuning learning rate to speed-up convergence or applying drop-out for avoiding overfit, etc... It shows you the under-the-hood theory and brings you the knowledge to grasp the basics yourself, and actually can apply back into your projects.

автор: WALEED E

8 янв. 2019 г.

The course is very useful for being acquainted with tuning hyper-parameters and modern optimization algorithms like momentum, RMSProp an Adam. It is also introducing how to prevent over-fitting efficiently from recent papers in addition to mini batching training data. Although it introduces TensorFlow in a brief way, the overall assessment needs some revision.

автор: Basel O

8 нояб. 2020 г.

This course was as the first one. Nothing new, extremely interesting with Prof. Andrew. All assignments are such amazing. I like the way how they are formatted. It gives us a golden chance to revise theory and apply it immediately. Everything was just right. Thank you Andrew and TAs and all people who helped making this course looks the way what it looks now.

автор: Ruthuparna K

9 июля 2020 г.

Gives you an in-depth understanding on how to finetune your neural network hyperparameters and introduces you to the various optimization methods. Finally, an introduction to TensorFlow gives a more practical solution to developing your code fast and easy. Yet again, Andrew Ng is nothing short of brilliant and his ML content is always the best in the world.

автор: 石啸

15 февр. 2020 г.

I strongly recommend this course since I pass an interview after finish the first and second specialization. Although it is not enough for some high-demanded company, it is a really good lecture and experience for the new beginner in neural networks. But I have to say that the project is too easy so far, I wish we will have more great exercises and projects!

автор: Saimur R A

2 авг. 2020 г.

This course trully go deeper into the deep learning and I learned a lot of things which improve my concept about NN network. Andrew gave an excellent lesson like the first course and simplify everything and the quote from Andrew "if you dont understand anythink don't worry too much about it" really make sense and over the time the concept will get clearer.

автор: J A

8 сент. 2017 г.

Very clear, straight to the point, explanations with very well guided programming assignments in Python to hammer the concepts. A lot of knowledge and experience condensed in just a few hours and materials. I recommend previous exposure to Python and Machine Learning to make the most of this course (Ng's Coursera's course provides a very solid foundation)

автор: Amaranath B

13 окт. 2019 г.

This is an amazing course , the way they had designed the transition from numpy to tensorflow was amazing. The the concepts of gradient descent with momentum to adam optimizer was great coming from your previous course , I can't express how much this has grounded my understanding. I'm pushing myself to complete the specialization. Thanks a lot everyone !