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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
40,746개의 평가
4,339개의 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

HD

Dec 06, 2019

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse

AM

Oct 09, 2019

I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,269개 리뷰 중 226~250

교육 기관: Sourav

Jan 05, 2019

Learnt a great deal about tuning models. Concepts of regularization, batch norm and optimizers were very well explained.

교육 기관: Sarfaraz K

Jan 19, 2019

Very well organized course by a great teacher

교육 기관: Amir K

Jan 18, 2019

To the point and effective!

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: David W

Jan 19, 2019

very thoughtful introduction to various learning optimizer. easy introduction into tensorflow.

it would be better if there is more content on the local optima/saddle point issue.

교육 기관: Raunak N

Jan 19, 2019

Thanks for such a remarkable teaching

교육 기관: Mohammed U

Jan 19, 2019

Excellent Support and course materials.

교육 기관: Fernando G

Jan 19, 2019

Exceptional course! Very interesting and illustrative. Only problem I had was with the Tensorflow notebook.

교육 기관: Juilee D

Jan 20, 2019

very elegant course, with nicely structured assignments and study material

교육 기관: Shah M D

Jan 20, 2019

Great Course. This course does explain some optimisation algorithm with quit a good detail. That is a good part of it. Many less courses explain those algorithms at a level of abstraction an undergraduate student needs. Also, it shows the usage of tensorflow, which is used by major practitioners.

교육 기관: Ajay S

Jan 07, 2019

Really a Great course for the deep learning thanks coursera for prioviding me financial aid for the course and i am able to complete the course with in the time . Thanks a Lot .

교육 기관: AlexZhao

Jan 08, 2019

Awesome course

교육 기관: WALEED E

Jan 08, 2019

The course is very useful for being acquainted with tuning hyper-parameters and modern optimization algorithms like momentum, RMSProp an Adam. It is also introducing how to prevent over-fitting efficiently from recent papers in addition to mini batching training data. Although it introduces TensorFlow in a brief way, the overall assessment needs some revision.

교육 기관: Dhananjaykuamr o

Jan 08, 2019

great course

교육 기관: Lim K Z

Jan 09, 2019

Really love your interviews with the prominent figures in the AI sphere - very inspiring and insightful. Particularly like the advice given by Yoshua Bengio. Keep it up!!!!

교육 기관: Syed M H J

Jan 09, 2019

Easily the best course on diving under the hood of how a Neural Network actually works and how to tune to the satisfaction of our results.

A no brainer for sure. The best part the exercises. You MUST do the exercises to understand thoroughly how the systems actually work.

교육 기관: KimYunSu

Jan 09, 2019

I liked it!

교육 기관: Aakash G

Jan 08, 2019

Started with some basic tensors... so that's good. However I like how Andrew explains the effect of each hyper-parameter on the models output. Happy learning!

교육 기관: xuezhibo

Jan 20, 2019

nice

교육 기관: liuyaqiu

Jan 21, 2019

A good course for deep learning novice.

교육 기관: Edoardo S

Jan 20, 2019

Very impressive course, really well done and interesting. One suggestion: apart from the modelling part in the programming assignment, I would also introduce some coding about the computing of the results and the final cost plot (in all the programming assignment these parts are already pre-compiled)

교육 기관: Caroline K

Jan 21, 2019

Great sequel to course 1 for AI beginners.

교육 기관: ABHISHEK B

Jan 21, 2019

awesome

교육 기관: dmitry p

Jan 09, 2019

Good course, just jupyter notebook hangs

교육 기관: Yannik L

Jan 09, 2019

Excellent