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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

42,913개의 평가
4,599개의 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


Dec 06, 2019

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse


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.

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,540개 리뷰 중 351~375

교육 기관: Angelo C

May 06, 2019

Enjoyed the class and would recommend to those who wants to know more about the hyperparameters relating deep learning. Materials well explained by Prof Ng and assignment equally well designed. Looking forward to the next section.

교육 기관: Sagar K

Mar 19, 2019

Liked the content of this course. I would have liked optional videos about the mathematics behind the optimization algorithms. Appreciate the focus on building the optimization algorithms from ground up before learning a framework.

교육 기관: Anand R

Feb 23, 2018

A great course. I appreciate the way how Andrew Ng explained all the technical details which i have never able to understand. Before taking this course, it used to be black box for me. Many many thanks to the great teacher of AI.

교육 기관: Enrico

Sep 25, 2019

Andrew NG is masterful at explaining complex things in the clearest possible way teaching all and only you need to get an understanding of the subject that is good and complete relative to the goals of the course. Amazing teacher.

교육 기관: Eymard P

Jul 16, 2018

Very well explained and detailed. The less positive aspect is that I think the programming assignements are a bit too easy. But for the rest it's perfect, it's always interesting and clear. Thank you for the high quality content !

교육 기관: Bharath K

Sep 28, 2017

Nuts & Bolts of deep learning were very well explained thought in this course which will be very useful in building a robust neural networks. Maths behind the concepts were explained clearly. Thank you very much Prof.Andrew Ng !

교육 기관: Jinxiang R

May 19, 2019

I am so grateful that Andrew and his team provide such great course, after completed the course now I have more understanding about different optimizer and regularization methods of the NN. And practical exercise with tensorflow

교육 기관: Rahul v

Sep 04, 2018

This course is awesome. In the end of this I can understand the how to make your model more efficient and optimal. How I can play with our training set and how to improve the our Deep Neural Network.

Thank you so much Andrew sir.

교육 기관: Ai-sawan J

Apr 02, 2018

I like the way Prof. Andrew explains intuitions and how Momentum works in Deep Learning. Also, this course gives practical explanations of how improve models. I would recommend to anyone who want to start learning Deep Learning!

교육 기관: Dixant M

Oct 06, 2017

All the techniques taught were very effective. Before this course, I made a NN without knowing these techniques and it was a pain to get it to converge. Hopefully, after applying these techniques. it is performing very well now.

교육 기관: Steve A

May 26, 2018

Good stuff. All in all well worth the few weeks to get a better idea of how to thing and deal with parameters.

I feel like I need a real course on tensorflow though. Documentation and tutorials are not googles strong point.

교육 기관: Travis J

Mar 19, 2018

Very rich with information on various ways Neural Network training can benefit from optimizations. I'm sure there are many more optimizations to explore, and this serves as a great introduction to some of the more common ones.

교육 기관: Daniel D

Sep 03, 2017

The optimization algorithms and the the introduction to tensorflow were the topics I liked the most. Although hyperparameter tuning is important, this seems to me to be still very empirical. Also, more interviews would be nice.

교육 기관: Alvin A

Aug 23, 2017

In this course, Professor Ng shares great guidelines on tuning deep learning hyperparameters, which are a lot compared to other machine learning algorithms. This will surely help any deep learning projects to be more effective.

교육 기관: Ferdi A

Jan 16, 2020

Adjusting parameters are highly essential skills for deep learning programming that most of my friends lacking. Great lectures and assignments around the topic, many thanks to the lecturer and assistants for their great works.

교육 기관: DHATRI M P

Jan 07, 2020

The course will make all concepts about improving deep neural network understand in excellent manner by Andrew Ng.Must complete the course on concepts along with applciaitons are clearly explained.

교육 기관: Debabrata M

Jun 23, 2019

This course is an absolute necessary for anyone who wants an in-depth knowledge of optimising their deep learning solutions. Loved the course work and I could easily relate the course contents with the practical aspects of AI.

교육 기관: Samuel M

Oct 23, 2018

Excellent follow up to the first course. Lectures and lessons are well matched to reinforce the material. A few minor errors in the programming assignments that have been pointed out in the forums that need to be corrected.

교육 기관: Suresh K

May 05, 2018

Really great course by Andrew. I am marking all of them as 5 stars. But these are not fake reviews. These are really great. As I have mentioned in other comments. I really like the style of Andrew of writing while explaining.

교육 기관: Rangaraj S

Oct 08, 2017

Learnt a lot about the different of the Hyper-Parameters & the different kinds of Optimization algorithms. Was really beautifully explained & made intuitive to understand. Loved to have an introduction to Tensor-Flow as well.

교육 기관: William G

Jul 05, 2019

A little less technical than the first machine learning course for the Deep Learning Specialization, but very valuable nonetheless, don't hesitate to try! It truly is a good course and Professor Andrew Ng is a great teacher!

교육 기관: Robert P

Apr 17, 2018

The content is generally great and well worth it. Perhaps the only frustrating aspect is navigating to the Jupyter notebooks. I wish the links to the notebooks were on the same pages as the Submission and Discussion links.

교육 기관: Hilla P

Dec 28, 2017

Again Andrew Ng did a fantastic job explaining complex problems in a simple terms which make the course fun to follow. The quiz and the practice exams also help better understand the problem and the concept behind the video.

교육 기관: Mikhail G

Oct 31, 2017

Very helpful course that sheds light on the inherent parameters of popular 'black box' DL libraries. After passing the course you will be able to understand and variate the majority of those small but important coefficients.

교육 기관: Tao H

Aug 21, 2017

Very helpful! This course helps me step into the details of deep neural networks in practice, and teaches me how to fix those issues, as well as Tensorflow which is a popular deep learning programming framework using Python.