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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization(으)로 돌아가기

deeplearning.ai의 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 학습자 리뷰 및 피드백

40,004개의 평가
4,259개의 리뷰

강좌 소개

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....

최상위 리뷰


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.


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.

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,187개 리뷰 중 251~275

교육 기관: Judith G

Mar 17, 2019

Andrew is not only a well known practitioner in the ML field, he is also an excellent teacher. His class has been designed to let people learn the subject in a very efficient way!

교육 기관: Fred K

Mar 18, 2019

Very clearly explained, covers the underpinnings of a lot of the most used algorithms.

교육 기관: Aishwarya R

Mar 19, 2019 courses are worth the time and effort

교육 기관: Sagar K

Mar 19, 2019

Liked the content of this course. I would have liked optional videos about the mathematics behind the optimization algorithms. Appreciate the focus on building the optimization algorithms from ground up before learning a framework.

교육 기관: Regi M

Mar 20, 2019

The subject was covered in amazing detail. The highlight was that the great balance of concepts and practice.

교육 기관: Jan N

Mar 20, 2019


교육 기관: Emilio J

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.

교육 기관: Rahul S

Mar 20, 2019

Amazing explanation and examples made the course so much interesting, completed a week course in a day.

교육 기관: KI B C

Mar 20, 2019

excellent course !

교육 기관: Ayon B

Mar 19, 2019

Good explanation of concepts

교육 기관: Walker J

Mar 19, 2019

Great course that has thought me a lot about building practical deep learning models.

교육 기관: ChangIk C

Mar 20, 2019

Just like all other Professor Andrew's class, best

교육 기관: Aniruddha S H

Mar 19, 2019

this was amazing. Explained almost all the hyperparameters, why we need those and how can we optimizat those. along with regularization, dropouts. Everything was explained from need to the hands on implementation.

교육 기관: Ali S

Mar 19, 2019

This is a great course like other ones in this specialization. I learned from this course why we need regularization, how to do them exactly, what are the rules-of-thumb for setting hyperparameters, and how to find them systematically.

교육 기관: 唐章源

Mar 27, 2019


교육 기관: KEMAL S

Mar 27, 2019

These courses gives a new aspect of life. I gained lots of valuable information.

교육 기관: HEF

Mar 27, 2019

This course taught me a lot of things that I cannot usually find in a school curriculum, yet the content are extremely useful in helping me to accelerate my algorithms. This course is super important in handling deep learning projects, I think.

교육 기관: Celia C

Mar 27, 2019

Hope the tensorflow homework can be more clearly instructed. And hope there were more tensorflow part of homework

교육 기관: Jose P

Mar 22, 2019

This course, so far is surprisingly useful and well explained

교육 기관: 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.

교육 기관: 용석 권

Mar 22, 2019


교육 기관: Alper B

Mar 23, 2019

this is best course i've taken

교육 기관: Pedro f

Mar 23, 2019

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

교육 기관: Saurabh S

Mar 24, 2019

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

교육 기관: Johnson J

Mar 25, 2019

Awesome course! Andrew explained optimizers like RMSProp and Adam very clearly. I learned a lot!