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.Lee mas.
Este recurso es ofrecido por un socio afiliado. Si paga por la capacitación, podemos ganar una comisión para respaldar este sitio.
Las técnicas y herramientas cubiertas en Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization son muy similares a los requisitos que se encuentran en los anuncios de trabajo de Científico de datos.
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization is a part of uno structured learning path.
5 Courses
5 Months
Deep Learning
