改善深度神经网络:超参数调整、正则化和优化

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

506 次查看
deeplearning.ai
Coursera
  • 完成时间大约为 15 个小时
  • 简单(初级)
  • 英语, 中文, 韩语, 其他, 西班牙语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

课程概况

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.

课程大纲

Practical aspects of Deep Learning

Optimization algorithms

Hyperparameter tuning, Batch Normalization and Programming Frameworks

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