Chevron Left
返回到 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, deeplearning.ai

4.9
30,709 个评分
3,352 个审阅

课程信息

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....

热门审阅

创建者 CV

Dec 24, 2017

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.

创建者 NC

Jun 03, 2018

Just as great as the previous course. I feel like I have a much better chance at figuring out what to do to improve the performance of a neural network and TensorFlow makes much more sense to me now.

筛选依据:

3,287 个审阅

创建者 Saurabh Sawarkar

Mar 24, 2019

Awesome course. Improving the Deep Neural Network performance is explained so intuitively.

创建者 Alper Babadağ

Mar 23, 2019

this is best course i've taken

创建者 Pedro fernandez

Mar 23, 2019

Easy understand in a very complex deep learning techniques. Professor Andrew transmits his deep knowledges in a clear and simple way

创建者 Khosro (Roy) Pichka

Mar 22, 2019

Great course but homework assignments are a bit confusing!

创建者 Jose Perez-Macias

Mar 22, 2019

This course, so far is surprisingly useful and well explained

创建者 용석 권

Mar 22, 2019

Good

创建者 Hichem

Mar 22, 2019

Very good course, with most importantly intuitions given and also some (superficial) theory underlying the principles of NN and other stuff.

创建者 Ilkhom

Mar 21, 2019

awful sound

创建者 Vishal

Mar 21, 2019

Tough Concepts are not explained clearly like dropout regularization

创建者 Emilio José

Mar 20, 2019

El curso está muy bien impartido por Andrew NG y te permite adquirir muy rápido conocimientos sobre los puntos clave para mejorar el aprendizaje con redes neuronales de una forma genérica. La práctica de programación con la plataforma tensorflow de python es muy valiosa, aunque se hecha de menos una mayor profundidad en el uso de las herramientas disponibles de tensorflow y otras utilidades de python para redes neuronales. El curso utiliza como ejemplos didácticos y prácticas la aplicación de redes neuronales al reconocimiento de imágnes, pero estaría bien ampliar los ejemplos con aplicaciones prácticas a otros campos como puede ser un modelado de un proceso físico.