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

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, deeplearning.ai

4.9
Оценки: 29,061
Рецензии: 3,218

Об этом курсе

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....

Лучшие рецензии

автор: PG

Oct 31, 2017

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.

автор: 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.

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Рецензии: 3,163

автор: Claudio Coppola

Feb 18, 2019

very useful also for experts

автор: Jorge de Jesus Gomes Leandro

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.

автор: William Farkas

Feb 17, 2019

Good solid theory and practice advancing my understanding of deep learning and an effective intro to TensorFlow

автор: Stan Yamane

Feb 17, 2019

Great introduction to practical aspects of tuning and optimizing a deep learning network.

автор: Shankar N

Feb 17, 2019

Could have been a 4 week course

автор: Khongorzul Ganbold

Feb 17, 2019

one word: amazing!

автор: Nicolo de Groot

Feb 17, 2019

A bit short and light to be a course on its own but still useful in the series.

автор: Myunggwan Cho

Feb 17, 2019

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: Md. Yousuf Harun

Feb 17, 2019

I learned a lot from this course. Thank you so much for offering such thorough course.

автор: Erick Adinugraha

Feb 16, 2019

Great, really enjoy it.