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