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Вернуться к Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

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

4.9
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Оценки: 60,692
Рецензии: 7,031

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

JS

4 апр. 2021 г.

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.

NA

13 янв. 2020 г.

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.

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426–450 из 6,977 отзывов о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

автор: Nazmus S E

18 апр. 2020 г.

This is one of the best courses on Coursera. Cleared a lot of concepts. Before this course, I was always thinking, what to do if I had to classify among multiple classes, but the explanation of softmax was actually very helpful in answering that question.

автор: ANSHUMAN S

25 мая 2019 г.

This was a very interesting and different course from others. I found it very helpful

for improving the NNs and the techniques taught with assignments give a well insight so as to how the problem should be dealt with.

Thank you to teachers and to Coursera.

автор: Vivek V

6 нояб. 2017 г.

A perfect course on Deep learning. Mathematical analysis well put forward by Andrew. I am looking forward to finish Deep Learning specialization. I would appreciate if he provides reference to textbooks to learn more about the fundamentals.

Thank you,

Vivek

автор: Yash R

29 нояб. 2021 г.

Great course. It goes over many practical method to speed up the training process and also does an excellent job at explaining why these algorithm work. The programming assignment notebooks were also nice and helps to reinforce and understand the theory.

автор: Jiani S

4 янв. 2020 г.

Recommend! The parts of batch norm and epoch in mini-batch solved my confusions. And the exercise of Tensorflow is simplify and useful. Without tedious documents, you can easily contruct a neural network for practical problems following the instructions.

автор: Satyam D

12 дек. 2018 г.

Yet another great course from Prof. Andrew Ng and Coursera. Deeply grateful to all involved in the preparation of this course. Absolutely essential to learn these concepts if we want to build and optimize deep neural networks for creating great products!

автор: Kyle L

23 дек. 2017 г.

The conciseness of the course material and interviews with industry experts offer thorough insight and can inspire confidence in new and old DNN learners alike. I look forward to learning more in the remaining courses of the Deep Learning Specialization!

автор: Hao X

30 авг. 2020 г.

I benefit a lot from this course regarding parameter initialization, hyperparameter optimization, batch normalization, optimization, etc. All the knowledge are well explained both intuitively and mathematically. Always enjoyable to learn from Andrew Ng.

автор: Naman S

23 июля 2020 г.

As expected from deeplearning.ai and Sir Andrew Ng, this course was really great!

Choosing between so many hyper-parameters and tuning them can be a confusing task, but this course explains each one in detail and simply.

Thank you for this amazing course!

автор: Camilo M

23 июня 2020 г.

Not long ago I have been working with machine learning and artificial networks, but without a doubt so far I think my learning curve has been exponential. Themed content is for anyone to fall in love with technology and climb on the shoulders of giants.

автор: Akash R

16 дек. 2018 г.

I understood the bits and pieces of how tuning particular hyperparameters will lead to a great improvement model being developed. Andrew NG was great to teach everything with examples and deep dive into the concepts. Thanks for the opportunity provided.

автор: Ravi K

19 дек. 2017 г.

Great course on practical techniques to tune hyper parameters. Great to see the practical experience driven lectures with succinct, focussed theory. Learning has never been so exciting. Kudos for the team who put together such as great code scaffolding.

автор: Keith T

28 окт. 2017 г.

Andrew Ng picks up where he left off with 'Machine Learning' and takes a relatively complicated subject and breaks it down into clear an understandable blocks, providing insight and intuition along the way. But that's what great teachers do well, right?

автор: Myunggwan C

17 февр. 2019 г.

I'm on the road to improvement with my deep learning skills with the current specialization.

Thank you for providing such a great quality course online.

I also appreciate the mentors who comment to every post in discussion group.

Keep up the good work!

автор: Ankit M

13 нояб. 2017 г.

Very wonderful and thought provoking stuff has been provided for learning optimization and regularization. Latest stuff have been used to demonstrate the examples. Thanks to Coursera for providing a good platform to learn all this tools and techniques.

автор: Akella N

29 авг. 2020 г.

This is a fabulous course for hyperparameter tuning, regularization, normalization, optimization, and other tensorflow framework commands. I have gained immense knowledge from this course. My deep regards to Andrew Ng et. al. for such a worthy course.

автор: govind b

9 мая 2020 г.

This course is good for those who want to learnt about the different regularization technique and the most important optimizer algorithms. The course material is good and easy to understand. I liked this course so much and it teach me lots of things.

автор: J.-F. R

18 февр. 2020 г.

Great course by Prof Ng. I had taken his Machine Learning course a few years ago, so expected high standards of content and assignment preparation - I was not disappointed. Staff is very responsive and helpful in forums as well. I highly recommend it.

автор: Rohit G

18 сент. 2017 г.

In-depth learning about Hyper-parameter Tuning, multiple Regularization and Optimization Techniques. This Course makes learning Deep Learning Framework like Tensorflow very easy.

All thanks to Andrew NG and team for building this course so interactive.

автор: Nikesh P

22 янв. 2019 г.

Hyperparameters can affect our parameters and how tuning them properly would speed up our optimization is nicely taught. And it was great to know the intuition and mathematics behind other optimization algorithmswhich which was also taught very well.

автор: MONIL J

10 июня 2020 г.

It's been a great learning through out this course. Even by improving hyperparameters, tuning them and optimizing them, we can increase the efficiency and reduce the execution time very much.

Thank you so much Andrew Ng sir for this amazing course!!!

автор: Victor

16 янв. 2020 г.

This course is very useful, a good extension of the first deep learning course. Like the name of the course, on top of the basic neural networks knowledge, this course focus on how to improve the performance of the fundamental neural networks built.

автор: Navy X

24 сент. 2017 г.

In the course there are lots of impressed optimization strategies that are hard to get outside from here. I was shocked when I saw the performance improvement after I run He initialization and Adam, according to the instruction in the course. Great!

автор: Okta F S

24 мар. 2020 г.

I'm very happy can finish this course. After taking this course, I understand how to optimize deep learning model, how to tuning hyperparameters. Also, by doing the programming assignment I can understand how to do optimization process from scratch

автор: TANVEER M

12 июля 2019 г.

THE COURSE TAUGHT ME DIFFERENCE BETWEEN RMS ,MOMENTUM AND ADAM OPTIMISER.WHY BATCH NORMALISATION IS IMPORTANT.HOW REGULARISSATION AND DROPOUT IS USEFUL.GRADIENT DESCENT AND MINI BATCH DESCENT AND WHY IT SHOULD BE BETWEEN 1 AND NUMBER OF M(SAMPLES).