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.阅读更多.
此资源由附属合作伙伴提供。 如果您支付培训费用，我们可能会赚取佣金来支持该网站。
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 中涵盖的技术和工具与 数据科学家 招聘广告中的要求最为相似。
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization is a part of 一 structured learning path.
5 Courses
5 Months
Deep Learning
