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

31,803 notes
3,440 avis

À propos de ce cours

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....

Meilleurs avis

par 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.

par 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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3,376 avis

par tingting zhang

Apr 19, 2019


par Matthew Glass

Apr 18, 2019

Very good course. Andrew really steps it up in part two with lots of valuable information.

par Rafael Araujo

Apr 17, 2019

Excelente curso, recomendo fortemente, principalmente pela base matemática fornecida.

par Jenil Mehta

Apr 17, 2019

nice course

par 紫色人的心

Apr 15, 2019


par Md. Redwan Karim Sony

Apr 15, 2019

Excellent course. When I learned about implementing ANN using keras in python, I just followed some tutorials but didn't understand the tradeoff among many parameters like the number of layers, nodes per layers, epochs, batch size, etc. This course is helping me a lot to understand them. Great work Mr. Andrew Ng. :)

par Tang Yuanyuan

Apr 15, 2019

very practical.

par 董云鹏

Apr 14, 2019

very useful

par Mohamadreza

Apr 14, 2019

That was AWSOME! It was really good and I'm pretty sure it would help me through my study and career!


par 郑笙桦

Apr 14, 2019

Means a lot.