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Вернуться к Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Отзывы учащихся о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization от партнера

Оценки: 60,677
Рецензии: 7,028

О курсе

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Лучшие рецензии


13 янв. 2020 г.

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.


18 апр. 2020 г.

Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course

Фильтр по:

6901–6925 из 6,976 отзывов о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

автор: Rajat K S

11 янв. 2020 г.

Most of the solutions to the assignment were written in instructions.

автор: Ganesan G

28 дек. 2017 г.

I am not getting to see the programming exercises that i have done :(

автор: Jonghyun K

25 апр. 2020 г.

voice was too small compared to noises made by clothes and others.

автор: Aastha S

14 июля 2021 г.

More explanations required for functions used in tensorflow lab


13 мая 2018 г.

Good courses, the sound quality is very poor (high tone noise).

автор: Suhas M

20 янв. 2019 г.

Interface for evaluating is not great and assignments are easy

автор: Alex I E

4 сент. 2017 г.

The Tensorflow part should have started sooner in the course.

автор: Aloys N

1 июля 2019 г.

We could have more guidance on setting a tensorflow model


30 апр. 2018 г.

Lots of theory and not enough practical implementation.

автор: Stefan S

22 сент. 2020 г.

Content starts to feel old, but still interesting.

автор: Hasnaa T

10 февр. 2020 г.

the circulum was some hard and over detailed

автор: luca m

5 мая 2020 г.

I would have loved to have a session on TF2

автор: Kenneth C V

29 авг. 2019 г.

Course is a bit complex due to the subject

автор: Kartheek

1 февр. 2019 г.

week 3 topics would have been a bit better

автор: Tushar B

12 июня 2018 г.

Assignments vs lecture, difference is huge

автор: Aashita G

1 июня 2020 г.

fast paced not enough emphasis on topics

автор: Amod J

18 мар. 2018 г.

Want to download my own work but cannot.

автор: Rachana O

17 авг. 2020 г.

Can be done in more interesting manner.

автор: Mark L

16 июля 2020 г.

great superficial intro to the content

автор: Jérôme C

14 окт. 2018 г.

Need more training on Tensorflow, imho

автор: Juan J D

11 сент. 2017 г.

tensorflow subject was to superficial

автор: Weeha G

25 июля 2021 г.

Assignment of week 3 is toooo brief.

автор: SATHVIK S

26 июля 2020 г.

Can dive deeper into the mathematics

автор: Trevor M

23 нояб. 2020 г.

good lectures terrible exercises

автор: Maisam S W

4 окт. 2017 г.

I still find tensorflow hard.