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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

4.9
39,861개의 평가
4,245개의 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

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.

AO

Apr 06, 2018

Fantastic course! For the first time, I now have a better intuition for optimizing and tuning hyperparameters used for deep neural networks.I got motivated to learn more after completing this course.

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,179개 리뷰 중 26~50

교육 기관: Ananya R

Jan 27, 2019

Great Course, just like the rest in the specialisation! Really like the method of teaching!! Some sections of the assignment were coded before hand but would have been nice to be able to code them as well. Thanks

교육 기관: Aravind D C

Jan 28, 2019

Very good course to understand the nuts and bolts behind the deep learning

교육 기관: Jesus P

Jan 28, 2019

Great course

교육 기관: Sameer K

Jan 15, 2019

Oustanding

교육 기관: Abhishek B

Jan 16, 2019

Awesome Content and tutors!

교육 기관: John l

Jan 16, 2019

good course indeed

교육 기관: Yuting Z

Jan 14, 2019

its very useful!

교육 기관: Somex K G

Jan 15, 2019

this course helped me to understand, what is effect of various hyper-parameters and how to tune them.

교육 기관: Arjun R

Jan 15, 2019

Loved every lecture. Excellently structures programming assignments. Highly recommended for an beginner deep learning practitioner

교육 기관: Wei L

Jan 16, 2019

Fantastic course design!

교육 기관: David P

Jan 15, 2019

Very Useful!

교육 기관: Lakshmi N S

Jan 14, 2019

Nice course.

교육 기관: Ramon D R S

Jan 15, 2019

Un curso muy completo para introducirte a un nivel ams de DL

교육 기관: Subin J

Jan 15, 2019

I got a very good understanding about how to what are the parameters I can tune, regularization methods, and different gradient descent alternatives which can be used through this course.

교육 기관: Kirk B

Jan 17, 2019

Andrew Ng is hands down the best teacher in this space. Excellent lectures and a well run course.

교육 기관: Amir K

Jan 18, 2019

To the point and effective!

교육 기관: Raj

Jan 17, 2019

Awesome course.

교육 기관: Shravan M

Jan 17, 2019

Thank You!!!

교육 기관: Chen N

Jan 18, 2019

Awesome as always.

교육 기관: Elvis S

Jan 18, 2019

Loved this part of the code... it allowed me to understand more about the optimization and regularization tricks such as RMSprop and Dropout.

교육 기관: Rob

Jan 30, 2019

Really enjoyed the last section on TensorFlow

교육 기관: Patrick M

Jan 29, 2019

Thank you so much for this course. It's been really insightful and helpful

교육 기관: Dan L

Jan 30, 2019

great course - program assignments can be a bit harder... plus, maybe more tensorflow assignments can be really useful

교육 기관: Raymond T Q T

Jan 31, 2019

very good lectures

교육 기관: Juilee D

Jan 20, 2019

very elegant course, with nicely structured assignments and study material