Name: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Brand: DeepLearning.AI
Availability: OnlineOnly
Rating: 4.9 (7310 reviews)

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Learner Reviews & Feedback for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization by DeepLearning.AI

4.9

stars

62,961 ratings

About the Course

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....

Top reviews

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Yet another excellent course by Professor Ng! Really helped me gain a detailed understanding of optimization techniques such as RMSprop and Adam, as well as the inner workings of batch normalization.

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very useful course, especially the last tensorflow assignment. the only reason i gave 4 stars is due to the lack of practice on batchnorm, which i believe is one of the most usefule techniques lately.

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6926 - 6950 of 7,225 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Massimiliano L C

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Dec 19, 2019

Great course, incredibly useful

By Pavao S

•

Feb 11, 2018

I would like to see more theory

By Saad K

•

Sep 12, 2017

Could probably be more condense

By Yash A

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Nov 23, 2020