Автор курса:

Hyperparameter tuning, Regularization and Optimization

Улучшаемые навыки

Учёный по данным · Python · TensorFlow · Deep Learning

Где проходит обучение

Онлайн обучение

Начало учёбы и длительность

В любой момент

Стоимость

Бесплатно

Описание курса

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.