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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization に戻る

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



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....


by 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.

by XG

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.



by Xuecong Liu

Apr 25, 2019

Learnt a lot, thanks!

by Hiroaki Kubo

Apr 24, 2019


by Md Fahim Sikder

Apr 23, 2019

Thank you Coursera for this awesome course. I have learned a lot from this course. And the presentation is on the whole new level.

by Duong Dang Khoa

Apr 23, 2019

Thank you for your lectures. It's very very interesting and easy to understand

by 刘培臻

Apr 23, 2019

very good!!!!!

by Elpidio Emmanuel Gonzalez Valbuena

Apr 23, 2019

Great explanations on behind the scenes operations of optimization algorithms and general theory. Coming from a more practical background, it helped me grasp the concepts much better. I only wish the programming exercises were a little bit more challenging!

by Richard Guo

Apr 23, 2019

Really good basic theory explanation, I took Udacity Deep learning NanoDegree too, this one actually gives more detail explanation, Udacity is more like a higher level and more advanced project practice

by Surya Jayaraman

Apr 23, 2019

Great course to build intuition about tuning NN. Solid Foundation in very short duration.

by Youssouf BENYETTOU

Apr 22, 2019

what I did recognize in the deeplearning specialization that there are now further reading suggestions or reading syllabus like the other courses.

by Alexandros Anatolakis

Apr 22, 2019

Loving the specialisation so far. TensorFlow introduction very basic - could use another week maybe?