Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization


Free Online Course Title: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Free Online Course Offered by: DeepLearning.AI

Approximate Time To Complete Course: 18 hours

Course Level: Intermediate

Student Outcome After Completing Course: 41% started a new career

Student Outcome After Completing Course: 37% received a tangible career benefit

Student Outcome After Completing Course: 12% received a pay raise or promotion

Learning Method: 100% online


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.


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