Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Free Download. you will understand what drives performance, and be able to more systematically get good results.

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

This course will teach you the “magic” of getting deep learning to work well. Rather than the deep learning process being a black box.

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.

Data Pipelines with TensorFlow Data Services

SKILLS YOU WILL GAIN

  • Hyperparameter
  • Tensorflow
  • Hyperparameter Optimization
  • Deep Learning

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

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Content From: https://www.coursera.org/learn/deep-neural-network

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